AI Driving Olympics Home Challenges Submissions Jobs

Evaluator 374

evaluator374
ownerAndrea Censi
machineidsc-rudolf
processidsc-rudolf-18328
versionDC:3.1.58;DCR:3.2.14;DTS:3.0.30
first heard
last heard
statusinactive
# evaluating
# success526
# timeout2
# failed56
# error35
# aborted
# host-error
arm
x86_64
Mac
gpu available
Number of processors
Processor frequency (MHz)
Free % of processors
RAM total (MB)
RAM free (MB)
Disk (MB)
Disk available (MB)
Docker Hub
P1
P2
PI Camera
# Duckiebots
Map 3x3 avaiable
gpu cores
AIDO 2 Map LF public
AIDO 2 Map LF private
AIDO 2 Map LFV public
AIDO 2 Map LFV private
AIDO 2 Map LFVI public
AIDO 2 Map LFVI private
IPFS mountpoint /ipfs available
IPNS mountpoint /ipns available

Evaluator jobs

Job IDsubmissionuseruser labelchallengestepstatusup to dateevaluatordate starteddate completeddurationmessage
167702218daredevilRL on dynamic obstaclesaido1_LFV_r2_cont-v3step2-scoringsuccessyes3740:00:25(hidden)
survival_time_median8.566666666666647


other stats
episodes
details{"ep000": {"nsteps": 27, "reward": -39.12136309235184, "good_angle": 0.699579347409305, "survival_time": 0.8999999999999999, "traveled_tiles": 2, "valid_direction": 0.6666666666666665}, "ep001": {"nsteps": 134, "reward": -8.235069782406251, "good_angle": 3.950636051434453, "survival_time": 4.466666666666661, "traveled_tiles": 1, "valid_direction": 3.5666666666666607}, "ep002": {"nsteps": 500, "reward": -0.0033826206596568225, "good_angle": 13.802354680109918, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 13.066666666666665}, "ep003": {"nsteps": 257, "reward": -5.148141939181233, "good_angle": 6.872828049594068, "survival_time": 8.566666666666647, "traveled_tiles": 1, "valid_direction": 6.499999999999984}, "ep004": {"nsteps": 500, "reward": -0.16412667773617431, "good_angle": 13.713618087953602, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.999999999999986}, "ep005": {"nsteps": 34, "reward": -30.33052278693546, "good_angle": 0.09268280422374482, "survival_time": 1.1333333333333335, "traveled_tiles": 1, "valid_direction": 0.2333333333333336}, "ep006": {"nsteps": 500, "reward": 0.027726142491213975, "good_angle": 13.758358086328975, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.999999999999991}}
good_angle_max13.802354680109918
good_angle_mean7.555722443864867
good_angle_median6.872828049594068
good_angle_min0.09268280422374482
reward_max0.027726142491213975
reward_mean-11.853554393825627
reward_median-5.148141939181233
reward_min-39.12136309235184
survival_time_max16.666666666666654
survival_time_mean9.295238095238084
survival_time_min0.8999999999999999
traveled_tiles_max2
traveled_tiles_mean1.1428571428571428
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max13.066666666666665
valid_direction_mean7.147619047619041
valid_direction_median6.499999999999984
valid_direction_min0.2333333333333336
167522211lmandrileSim Il Pytorch hypeaido1_LFV_r2_cont-v3step4-vizsuccessyes3740:01:11(hidden)
driven_lanedir_consec_median0.4653036481226116
deviation-center-line_median0.04212074809435606
in-drivable-lane_median0


other stats
deviation-center-line_max0.07800419306246101
deviation-center-line_mean0.045789221858600904
deviation-center-line_min0.017572248256401034
deviation-heading_max0.3882898069928627
deviation-heading_mean0.12751726901714275
deviation-heading_median0.08964633662410583
deviation-heading_min0.012124931754186077
driven_any_max1.2112655969602364
driven_any_mean0.632253905764587
driven_any_median0.6056335416665559
driven_any_min0.20964177324371125
driven_lanedir_consec_max0.6009728862125733
driven_lanedir_consec_mean0.3811374901714039
driven_lanedir_consec_min0.09408158629216246
driven_lanedir_max0.6009728862125733
driven_lanedir_mean0.3811374901714039
driven_lanedir_median0.4653036481226116
driven_lanedir_min0.09408158629216246
in-drivable-lane_max1.400000000000001
in-drivable-lane_mean0.31428571428571456
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.2112655969602364, "driven_lanedir": 0.09408158629216246, "in-drivable-lane": 1.400000000000001, "deviation-heading": 0.3882898069928627, "deviation-center-line": 0.02893980908575276, "driven_lanedir_consec": 0.09408158629216246}, "ep001": {"driven_any": 0.8851552274294914, "driven_lanedir": 0.5153003676449381, "in-drivable-lane": 0.4333333333333341, "deviation-heading": 0.2552606217066649, "deviation-center-line": 0.055854587626981346, "driven_lanedir_consec": 0.5153003676449381}, "ep002": {"driven_any": 0.20964177324371125, "driven_lanedir": 0.20917597538978505, "in-drivable-lane": 0, "deviation-heading": 0.017464515091539776, "deviation-center-line": 0.017572248256401034, "driven_lanedir_consec": 0.20917597538978505}, "ep003": {"driven_any": 0.279523396215681, "driven_lanedir": 0.27934595531738715, "in-drivable-lane": 0, "deviation-heading": 0.012124931754186077, "deviation-center-line": 0.024216583236179704, "driven_lanedir_consec": 0.27934595531738715}, "ep004": {"driven_any": 0.46587142457141617, "driven_lanedir": 0.4653036481226116, "in-drivable-lane": 0, "deviation-heading": 0.030293324454907837, "deviation-center-line": 0.07381638364807444, "driven_lanedir_consec": 0.4653036481226116}, "ep005": {"driven_any": 0.6056335416665559, "driven_lanedir": 0.6009728862125733, "in-drivable-lane": 0, "deviation-heading": 0.08964633662410583, "deviation-center-line": 0.04212074809435606, "driven_lanedir_consec": 0.6009728862125733}, "ep006": {"driven_any": 0.7686863802650167, "driven_lanedir": 0.5037820122203698, "in-drivable-lane": 0.3666666666666669, "deviation-heading": 0.09954134649573217, "deviation-center-line": 0.07800419306246101, "driven_lanedir_consec": 0.5037820122203698}}
167452211lmandrileSim Il Pytorch hypeaido1_LFV_r2_cont-v3step1-simulationsuccessyes3740:01:35(hidden)
other stats
simulation-passed1
167432210lmandrileSim Il Pytorch hypeaido1_LFV_r2_cont-v3step1-simulationsuccessyes3740:01:27(hidden)
other stats
simulation-passed1
167422209lmandrileSim Il Pytorch hypeaido1_LFV_r2_cont-v3step4-vizsuccessyes3740:01:15(hidden)
driven_lanedir_consec_median0.4345167776448915
deviation-center-line_median0.03863834151777364
in-drivable-lane_median0


other stats
deviation-center-line_max0.09656677550372546
deviation-center-line_mean0.04564156077866228
deviation-center-line_min0.016798159364268435
deviation-heading_max0.40819985481454735
deviation-heading_mean0.15699537811957295
deviation-heading_median0.1209561867981906
deviation-heading_min0.012830065588937891
driven_any_max1.2112698356974798
driven_any_mean0.6422337582912272
driven_any_median0.6056291631010339
driven_any_min0.20963631751664089
driven_lanedir_consec_max0.7545357940586968
driven_lanedir_consec_mean0.41765453849592754
driven_lanedir_consec_min0.14583609778044226
driven_lanedir_max0.7545357940586968
driven_lanedir_mean0.41765453849592754
driven_lanedir_median0.4345167776448915
driven_lanedir_min0.14583609778044226
in-drivable-lane_max1.333333333333334
in-drivable-lane_mean0.27142857142857174
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.2112698356974798, "driven_lanedir": 0.14583609778044226, "in-drivable-lane": 1.333333333333334, "deviation-heading": 0.40819985481454735, "deviation-center-line": 0.03863834151777364, "driven_lanedir_consec": 0.14583609778044226}, "ep001": {"driven_any": 0.8851562346912453, "driven_lanedir": 0.5024663083094485, "in-drivable-lane": 0.4333333333333341, "deviation-heading": 0.27920120121197683, "deviation-center-line": 0.05793646727215804, "driven_lanedir_consec": 0.5024663083094485}, "ep002": {"driven_any": 0.20963631751664089, "driven_lanedir": 0.20925867367095696, "in-drivable-lane": 0, "deviation-heading": 0.012830065588937891, "deviation-center-line": 0.016798159364268435, "driven_lanedir_consec": 0.20925867367095696}, "ep003": {"driven_any": 0.2795209758735283, "driven_lanedir": 0.27932616679680833, "in-drivable-lane": 0, "deviation-heading": 0.01326439971012997, "deviation-center-line": 0.020728343346655596, "driven_lanedir_consec": 0.27932616679680833}, "ep004": {"driven_any": 0.4425718865294777, "driven_lanedir": 0.4345167776448915, "in-drivable-lane": 0, "deviation-heading": 0.0987510467805882, "deviation-center-line": 0.055261239517610555, "driven_lanedir_consec": 0.4345167776448915}, "ep005": {"driven_any": 0.6056291631010339, "driven_lanedir": 0.5976419512102483, "in-drivable-lane": 0, "deviation-heading": 0.1209561867981906, "deviation-center-line": 0.033561598928444215, "driven_lanedir_consec": 0.5976419512102483}, "ep006": {"driven_any": 0.8618518946291841, "driven_lanedir": 0.7545357940586968, "in-drivable-lane": 0.13333333333333375, "deviation-heading": 0.16576489193263982, "deviation-center-line": 0.09656677550372546, "driven_lanedir_consec": 0.7545357940586968}}
167402209lmandrileSim Il Pytorch hypeaido1_LFV_r2_cont-v3step2-scoringsuccessyes3740:00:25(hidden)
survival_time_median0.8999999999999999


other stats
episodes
details{"ep000": {"nsteps": 53, "reward": -20.090765987786483, "good_angle": 0.7066413630603008, "survival_time": 1.7666666666666688, "traveled_tiles": 3, "valid_direction": 0.6000000000000019}, "ep001": {"nsteps": 39, "reward": -27.99373017795957, "good_angle": 3.6574880010371777, "survival_time": 1.3000000000000007, "traveled_tiles": 2, "valid_direction": 0.833333333333334}, "ep002": {"nsteps": 10, "reward": -102.28492414653302, "good_angle": 0.0029610248993192424, "survival_time": 0.3333333333333333, "traveled_tiles": 2, "valid_direction": 0}, "ep003": {"nsteps": 13, "reward": -81.54048093924156, "good_angle": 0.0005527848906769884, "survival_time": 0.4333333333333333, "traveled_tiles": 2, "valid_direction": 0}, "ep004": {"nsteps": 20, "reward": -50.040832948684695, "good_angle": 0.02464014870848898, "survival_time": 0.6666666666666666, "traveled_tiles": 2, "valid_direction": 0}, "ep005": {"nsteps": 27, "reward": -36.72421399227999, "good_angle": 0.02362057656224537, "survival_time": 0.8999999999999999, "traveled_tiles": 2, "valid_direction": 0}, "ep006": {"nsteps": 38, "reward": -25.738596593470948, "good_angle": 0.13655262531528933, "survival_time": 1.2666666666666673, "traveled_tiles": 2, "valid_direction": 0.33333333333333404}}
good_angle_max3.6574880010371777
good_angle_mean0.6503509320676424
good_angle_median0.02464014870848898
good_angle_min0.0005527848906769884
reward_max-20.090765987786483
reward_mean-49.201934969422325
reward_median-36.72421399227999
reward_min-102.28492414653302
survival_time_max1.7666666666666688
survival_time_mean0.9523809523809528
survival_time_min0.3333333333333333
traveled_tiles_max3
traveled_tiles_mean2.142857142857143
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0.833333333333334
valid_direction_mean0.2523809523809528
valid_direction_median0
valid_direction_min0
166942078VincentMaiCopy of #5: sub 1033 by VincentMai (ROS-based Lane Following)aido1_LFV_r2-v3step4-vizsuccessyes3740:02:37(hidden)
driven_lanedir_consec_median2.9713195566134085
deviation-center-line_median0.24762341883710215
in-drivable-lane_median0.3333333333333333


other stats
deviation-center-line_max0.6382828875961549
deviation-center-line_mean0.3018440759679239
deviation-center-line_min0.02308806974072968
deviation-heading_max1.7189200087749372
deviation-heading_mean0.6490914316316635
deviation-heading_median0.5319393736507855
deviation-heading_min0.13209072592620189
driven_any_max8.797132346794236
driven_any_mean3.9843063873511255
driven_any_median3.5741371708352405
driven_any_min0.42339493232426995
driven_lanedir_consec_max6.074544024466209
driven_lanedir_consec_mean3.1539638859092665
driven_lanedir_consec_min0.08882534010512133
driven_lanedir_max7.143750271462578
driven_lanedir_mean3.3067076354801768
driven_lanedir_median2.9713195566134085
driven_lanedir_min0.08882534010512133
in-drivable-lane_max1.0999999999999976
in-drivable-lane_mean0.4809523809523796
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.42339493232426995, "driven_lanedir": 0.08882534010512133, "in-drivable-lane": 0.3333333333333333, "deviation-heading": 0.13209072592620189, "deviation-center-line": 0.02308806974072968, "driven_lanedir_consec": 0.08882534010512133}, "ep001": {"driven_any": 6.540547271778574, "driven_lanedir": 5.0825315266491815, "in-drivable-lane": 1.099999999999996, "deviation-heading": 0.9833456775397676, "deviation-center-line": 0.5112790113318901, "driven_lanedir_consec": 5.0825315266491815}, "ep002": {"driven_any": 3.5741371708352405, "driven_lanedir": 2.5007032432525027, "in-drivable-lane": 0.8333333333333304, "deviation-heading": 0.5319393736507855, "deviation-center-line": 0.19426768574278425, "driven_lanedir_consec": 2.5007032432525027}, "ep003": {"driven_any": 8.797132346794236, "driven_lanedir": 7.143750271462578, "in-drivable-lane": 1.0999999999999976, "deviation-heading": 1.7189200087749372, "deviation-center-line": 0.6382828875961549, "driven_lanedir_consec": 6.074544024466209}, "ep004": {"driven_any": 3.0765961478333548, "driven_lanedir": 2.9713195566134085, "in-drivable-lane": 0, "deviation-heading": 0.5548502448776778, "deviation-center-line": 0.24762341883710215, "driven_lanedir_consec": 2.9713195566134085}, "ep005": {"driven_any": 4.948974948818735, "driven_lanedir": 4.860279306069157, "in-drivable-lane": 0, "deviation-heading": 0.47982320644085913, "deviation-center-line": 0.4562275445236578, "driven_lanedir_consec": 4.860279306069157}, "ep006": {"driven_any": 0.5293618930734624, "driven_lanedir": 0.4995442042092879, "in-drivable-lane": 0, "deviation-heading": 0.1426707842114152, "deviation-center-line": 0.0421399140031478, "driven_lanedir_consec": 0.4995442042092879}}
166812184Eric LuCopy of #53: sub 1311 by Eric Lu (Baseline solution using imitation learning from logs)aido1_LF1_r4-v3step4-vizsuccessyes3740:01:35(hidden)
driven_lanedir_consec_median0.5508639115473313
deviation-center-line_median0.1445521142456636
in-drivable-lane_median0.4999999999999982


other stats
deviation-center-line_max0.2657874941352664
deviation-center-line_mean0.1486761873583292
deviation-center-line_min0.06855649806194257
deviation-heading_max0.8151766090932518
deviation-heading_mean0.3142960564760511
deviation-heading_median0.162192325357688
deviation-heading_min0.140470287046866
driven_any_max1.1064522672769928
driven_any_mean0.7653654500122691
driven_any_median0.7919869082900299
driven_any_min0.43093399071545074
driven_lanedir_consec_max0.7975945006065916
driven_lanedir_consec_mean0.5112348360254085
driven_lanedir_consec_min0.11293902479039276
driven_lanedir_max0.7975945006065916
driven_lanedir_mean0.5112348360254085
driven_lanedir_median0.5508639115473313
driven_lanedir_min0.11293902479039276
in-drivable-lane_max2.4333333333333345
in-drivable-lane_mean0.6333333333333329
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.1064522672769928, "driven_lanedir": 0.11293902479039276, "in-drivable-lane": 2.4333333333333345, "deviation-heading": 0.8151766090932518, "deviation-center-line": 0.06855649806194257, "driven_lanedir_consec": 0.11293902479039276}, "ep001": {"driven_any": 0.5008151341100671, "driven_lanedir": 0.4973474628932339, "in-drivable-lane": 0, "deviation-heading": 0.162192325357688, "deviation-center-line": 0.16221477116159083, "driven_lanedir_consec": 0.4973474628932339}, "ep002": {"driven_any": 0.7919869082900299, "driven_lanedir": 0.5508639115473313, "in-drivable-lane": 0.6333333333333331, "deviation-heading": 0.31950558187668293, "deviation-center-line": 0.10899400049833136, "driven_lanedir_consec": 0.5508639115473313}, "ep003": {"driven_any": 0.6289307442639437, "driven_lanedir": 0.626677577153828, "in-drivable-lane": 0, "deviation-heading": 0.140470287046866, "deviation-center-line": 0.20924684963062387, "driven_lanedir_consec": 0.626677577153828}, "ep004": {"driven_any": 0.9783366815365392, "driven_lanedir": 0.7975945006065916, "in-drivable-lane": 0.4999999999999982, "deviation-heading": 0.15725933295197314, "deviation-center-line": 0.2657874941352664, "driven_lanedir_consec": 0.7975945006065916}, "ep005": {"driven_any": 0.43093399071545074, "driven_lanedir": 0.4276731911998677, "in-drivable-lane": 0, "deviation-heading": 0.1484085428053253, "deviation-center-line": 0.1445521142456636, "driven_lanedir_consec": 0.4276731911998677}, "ep006": {"driven_any": 0.9201024238928592, "driven_lanedir": 0.5655481839866141, "in-drivable-lane": 0.8666666666666647, "deviation-heading": 0.45705971620057045, "deviation-center-line": 0.08138158377488575, "driven_lanedir_consec": 0.5655481839866141}}
166442137BenjaminCopy of #6: sub 1063 by Benjamin (My ROS solution)aido1_LF1_r4-v3step4-vizsuccessyes3740:05:54(hidden)
driven_lanedir_consec_median13.262959942752277
deviation-center-line_median1.423393975861703
in-drivable-lane_median1.1333333333333315


other stats
deviation-center-line_max2.1437468389087004
deviation-center-line_mean1.2032846762552265
deviation-center-line_min0.14423974321431016
deviation-heading_max1.7351116526581984
deviation-heading_mean1.2467523616868217
deviation-heading_median1.3096121169076635
deviation-heading_min0.2738410397763178
driven_any_max15.668416921224209
driven_any_mean11.634659433781628
driven_any_median14.891710628737613
driven_any_min1.7361370943705423
driven_lanedir_consec_max15.548158379009116
driven_lanedir_consec_mean10.53250328435892
driven_lanedir_consec_min1.2184367893980117
driven_lanedir_max15.548158379009116
driven_lanedir_mean10.53250328435892
driven_lanedir_median13.262959942752277
driven_lanedir_min1.2184367893980117
in-drivable-lane_max1.8333333333333288
in-drivable-lane_mean1.1047619047619022
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.7361370943705423, "driven_lanedir": 1.2184367893980117, "in-drivable-lane": 0.6333333333333333, "deviation-heading": 0.2738410397763178, "deviation-center-line": 0.14423974321431016, "driven_lanedir_consec": 1.2184367893980117}, "ep001": {"driven_any": 15.050284385376932, "driven_lanedir": 13.938640404102346, "in-drivable-lane": 1.0333333333333297, "deviation-heading": 1.6570063982688894, "deviation-center-line": 1.4808857314401391, "driven_lanedir_consec": 13.938640404102346}, "ep002": {"driven_any": 11.072761059943767, "driven_lanedir": 9.605782317510116, "in-drivable-lane": 1.4999999999999956, "deviation-heading": 1.2165291384528136, "deviation-center-line": 1.0295254113817969, "driven_lanedir_consec": 9.605782317510116}, "ep003": {"driven_any": 7.947414019679802, "driven_lanedir": 6.8425597893681145, "in-drivable-lane": 1.1333333333333315, "deviation-heading": 0.9527517498537784, "deviation-center-line": 0.7239488433937199, "driven_lanedir_consec": 6.8425597893681145}, "ep004": {"driven_any": 14.891710628737613, "driven_lanedir": 13.262959942752277, "in-drivable-lane": 1.599999999999996, "deviation-heading": 1.7351116526581984, "deviation-center-line": 1.477252189586214, "driven_lanedir_consec": 13.262959942752277}, "ep005": {"driven_any": 15.07589192713853, "driven_lanedir": 13.31098536837244, "in-drivable-lane": 1.8333333333333288, "deviation-heading": 1.5824144358900902, "deviation-center-line": 1.423393975861703, "driven_lanedir_consec": 13.31098536837244}, "ep006": {"driven_any": 15.668416921224209, "driven_lanedir": 15.548158379009116, "in-drivable-lane": 0, "deviation-heading": 1.3096121169076635, "deviation-center-line": 2.1437468389087004, "driven_lanedir_consec": 15.548158379009116}}
166062134miksazCopy of #3: sub 1859 by miksaz (JetBrains Research)aido1_LF1_r4-v3step4-vizsuccessyes3740:04:15(hidden)
driven_lanedir_consec_median4.820666585242302
deviation-center-line_median0.5603265425589657
in-drivable-lane_median0.3999999999999986


other stats
deviation-center-line_max2.0584097747833163
deviation-center-line_mean1.019046189776833
deviation-center-line_min0.021579039423084992
deviation-heading_max1.4355724727489445
deviation-heading_mean0.901613699997327
deviation-heading_median1.195867436192223
deviation-heading_min0.21904177543400905
driven_any_max18.97871515802995
driven_any_mean9.63317905059497
driven_any_median5.283077877355814
driven_any_min0.11962813633739412
driven_lanedir_consec_max18.408035108458513
driven_lanedir_consec_mean9.11791029383008
driven_lanedir_consec_min0.025554610579865855
driven_lanedir_max18.408035108458513
driven_lanedir_mean9.15221704441495
driven_lanedir_median4.820666585242302
driven_lanedir_min0.025554610579865855
in-drivable-lane_max0.733333333333331
in-drivable-lane_mean0.38095238095237993
in-drivable-lane_min0.06666666666666665
per-episodes
details{"ep000": {"driven_any": 18.97871515802995, "driven_lanedir": 18.408035108458513, "in-drivable-lane": 0.4666666666666656, "deviation-heading": 1.258572245974052, "deviation-center-line": 2.0584097747833163, "driven_lanedir_consec": 18.408035108458513}, "ep001": {"driven_any": 18.76865281824138, "driven_lanedir": 17.893666347504904, "in-drivable-lane": 0.733333333333331, "deviation-heading": 1.4355724727489445, "deviation-center-line": 2.0557430176800495, "driven_lanedir_consec": 17.893666347504904}, "ep002": {"driven_any": 18.77842085144456, "driven_lanedir": 18.12962651572824, "in-drivable-lane": 0.5333333333333314, "deviation-heading": 1.3488225260588536, "deviation-center-line": 2.0389218396017195, "driven_lanedir_consec": 18.12962651572824}, "ep003": {"driven_any": 5.283077877355814, "driven_lanedir": 4.820666585242302, "in-drivable-lane": 0.3999999999999986, "deviation-heading": 0.41139948138548094, "deviation-center-line": 0.5603265425589657, "driven_lanedir_consec": 4.820666585242302}, "ep004": {"driven_any": 0.11962813633739412, "driven_lanedir": 0.025554610579865855, "in-drivable-lane": 0.06666666666666665, "deviation-heading": 0.21904177543400905, "deviation-center-line": 0.021579039423084992, "driven_lanedir_consec": 0.025554610579865855}, "ep005": {"driven_any": 4.70090968056005, "driven_lanedir": 4.304589752062856, "in-drivable-lane": 0.13333333333333286, "deviation-heading": 1.195867436192223, "deviation-center-line": 0.34787682020174526, "driven_lanedir_consec": 4.216200618713923}, "ep006": {"driven_any": 0.8028488321956386, "driven_lanedir": 0.4833803913279682, "in-drivable-lane": 0.3333333333333333, "deviation-heading": 0.4420199621877253, "deviation-center-line": 0.0504662941889501, "driven_lanedir_consec": 0.33162227058279514}}
166012099Maxim KuzminCopy of #26: sub 395 by Maxim Kuzmin (Random execution)aido1_LFV_r2-v3step4-vizsuccessyes3740:01:42(hidden)
driven_lanedir_consec_median0.5294426243193451
deviation-center-line_median0.12722906508312742
in-drivable-lane_median0.6333333333333324


other stats
deviation-center-line_max0.27631848554001326
deviation-center-line_mean0.14585689444595604
deviation-center-line_min0.05095367355034192
deviation-heading_max0.737453512127953
deviation-heading_mean0.4490968631924848
deviation-heading_median0.4875286041619871
deviation-heading_min0.11318866325833196
driven_any_max1.7616114140343906
driven_any_mean1.053968640323143
driven_any_median0.9894758603299718
driven_any_min0.4560635530260899
driven_lanedir_consec_max1.747827808063622
driven_lanedir_consec_mean0.7251512706333677
driven_lanedir_consec_min0.0785461929230582
driven_lanedir_max1.747827808063622
driven_lanedir_mean0.7251512706333677
driven_lanedir_median0.5294426243193451
driven_lanedir_min0.0785461929230582
in-drivable-lane_max2.366666666666668
in-drivable-lane_mean0.6238095238095236
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.2403571060362262, "driven_lanedir": 0.0785461929230582, "in-drivable-lane": 2.366666666666668, "deviation-heading": 0.6954290086808661, "deviation-center-line": 0.05095367355034192, "driven_lanedir_consec": 0.0785461929230582}, "ep001": {"driven_any": 0.8965685816008341, "driven_lanedir": 0.5294426243193451, "in-drivable-lane": 0.6666666666666674, "deviation-heading": 0.5687914833022131, "deviation-center-line": 0.11969383059330845, "driven_lanedir_consec": 0.5294426243193451}, "ep002": {"driven_any": 0.9894758603299718, "driven_lanedir": 0.5988391934928633, "in-drivable-lane": 0.6333333333333324, "deviation-heading": 0.737453512127953, "deviation-center-line": 0.17438682630948088, "driven_lanedir_consec": 0.5988391934928633}, "ep003": {"driven_any": 0.4663533536003529, "driven_lanedir": 0.46306167364686246, "in-drivable-lane": 0, "deviation-heading": 0.11642242190707566, "deviation-center-line": 0.09624166971304111, "driven_lanedir_consec": 0.46306167364686246}, "ep004": {"driven_any": 1.5673506136341342, "driven_lanedir": 1.205760672022366, "in-drivable-lane": 0.6999999999999975, "deviation-heading": 0.4875286041619871, "deviation-center-line": 0.1761747103323795, "driven_lanedir_consec": 1.205760672022366}, "ep005": {"driven_any": 0.4560635530260899, "driven_lanedir": 0.4525807299654572, "in-drivable-lane": 0, "deviation-heading": 0.11318866325833196, "deviation-center-line": 0.12722906508312742, "driven_lanedir_consec": 0.4525807299654572}, "ep006": {"driven_any": 1.7616114140343906, "driven_lanedir": 1.747827808063622, "in-drivable-lane": 0, "deviation-heading": 0.4248643489089668, "deviation-center-line": 0.27631848554001326, "driven_lanedir_consec": 1.747827808063622}}
165942143Allen OuCopy of #12: sub 590 by Allen Ou (Tensorflow template)aido1_LF1_r4-v3step4-vizsuccessyes3740:03:20(hidden)
driven_lanedir_consec_median5.103477918782273
deviation-center-line_median0.42982403948464265
in-drivable-lane_median0.033333333333333215


other stats
deviation-center-line_max1.1177213363740506
deviation-center-line_mean0.45635593389536216
deviation-center-line_min0.034399238980026385
deviation-heading_max2.063134627582097
deviation-heading_mean1.2686593512338322
deviation-heading_median1.6015780209519317
deviation-heading_min0.15835941082778038
driven_any_max8.638901736166657
driven_any_mean4.1625667136826285
driven_any_median5.5545698188248265
driven_any_min0.20307917378435156
driven_lanedir_consec_max8.507619609361052
driven_lanedir_consec_mean3.952793095449561
driven_lanedir_consec_min0.17268885591223126
driven_lanedir_max8.507619609361052
driven_lanedir_mean3.964908527452625
driven_lanedir_median5.1882859428037245
driven_lanedir_min0.17268885591223126
in-drivable-lane_max0.36666666666666664
in-drivable-lane_mean0.12857142857142842
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 5.840291592872388, "driven_lanedir": 5.38659751170166, "in-drivable-lane": 0.36666666666666664, "deviation-heading": 1.6153687740917066, "deviation-center-line": 0.42982403948464265, "driven_lanedir_consec": 5.38659751170166}, "ep001": {"driven_any": 5.5545698188248265, "driven_lanedir": 5.1882859428037245, "in-drivable-lane": 0.33333333333333304, "deviation-heading": 2.063134627582097, "deviation-center-line": 0.5883624280799811, "driven_lanedir_consec": 5.103477918782273}, "ep002": {"driven_any": 8.638901736166657, "driven_lanedir": 8.507619609361052, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 1.943382718464844, "deviation-center-line": 1.1177213363740506, "driven_lanedir_consec": 8.507619609361052}, "ep003": {"driven_any": 5.771152347325324, "driven_lanedir": 5.6386267073531, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 1.6015780209519317, "deviation-center-line": 0.6055464657185989, "driven_lanedir_consec": 5.6386267073531}, "ep004": {"driven_any": 1.2918920481105158, "driven_lanedir": 1.2131694104240005, "in-drivable-lane": 0.13333333333333286, "deviation-heading": 0.5636996088527138, "deviation-center-line": 0.23853954917653675, "driven_lanedir_consec": 1.2131694104240005}, "ep005": {"driven_any": 1.838080278694336, "driven_lanedir": 1.6473716546126045, "in-drivable-lane": 0, "deviation-heading": 0.9350922978657532, "deviation-center-line": 0.18009847945369828, "driven_lanedir_consec": 1.6473716546126045}, "ep006": {"driven_any": 0.20307917378435156, "driven_lanedir": 0.17268885591223126, "in-drivable-lane": 0, "deviation-heading": 0.15835941082778038, "deviation-center-line": 0.034399238980026385, "driven_lanedir_consec": 0.17268885591223126}}
165932127CpPICopy of #54: sub 798 by CpPI (Random execution)aido1_LFV_r2-v3step4-vizsuccessyes3740:01:28(hidden)
driven_lanedir_consec_median0.41907539058072674
deviation-center-line_median0.10679140599641736
in-drivable-lane_median0.6666666666666687


other stats
deviation-center-line_max0.12353370055982248
deviation-center-line_mean0.09798493685394025
deviation-center-line_min0.05700525218092768
deviation-heading_max0.6645358837187679
deviation-heading_mean0.4594039325694946
deviation-heading_median0.5127030134270567
deviation-heading_min0.0755176747894472
driven_any_max1.2753455667783973
driven_any_mean0.8784891904447614
driven_any_median0.887239910637587
driven_any_min0.25150516753431396
driven_lanedir_consec_max0.8531196457900734
driven_lanedir_consec_mean0.44817128710352383
driven_lanedir_consec_min0.1154099549952332
driven_lanedir_max0.8531196457900734
driven_lanedir_mean0.44817128710352383
driven_lanedir_median0.41907539058072674
driven_lanedir_min0.1154099549952332
in-drivable-lane_max2.3000000000000016
in-drivable-lane_mean0.8523809523809528
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.2304559160691997, "driven_lanedir": 0.1154099549952332, "in-drivable-lane": 2.3000000000000016, "deviation-heading": 0.6645358837187679, "deviation-center-line": 0.05700525218092768, "driven_lanedir_consec": 0.1154099549952332}, "ep001": {"driven_any": 0.887239910637587, "driven_lanedir": 0.5131914503756864, "in-drivable-lane": 0.6666666666666676, "deviation-heading": 0.5463543121983674, "deviation-center-line": 0.11156866746208952, "driven_lanedir_consec": 0.5131914503756864}, "ep002": {"driven_any": 0.98786481707156, "driven_lanedir": 0.624241830303597, "in-drivable-lane": 0.6999999999999993, "deviation-heading": 0.4614799119439836, "deviation-center-line": 0.11671359468124189, "driven_lanedir_consec": 0.624241830303597}, "ep003": {"driven_any": 0.25150516753431396, "driven_lanedir": 0.2491739190000244, "in-drivable-lane": 0, "deviation-heading": 0.0755176747894472, "deviation-center-line": 0.08257276457708322, "driven_lanedir_consec": 0.2491739190000244}, "ep004": {"driven_any": 0.7956410641483798, "driven_lanedir": 0.41907539058072674, "in-drivable-lane": 0.6666666666666687, "deviation-heading": 0.5127030134270567, "deviation-center-line": 0.10679140599641736, "driven_lanedir_consec": 0.41907539058072674}, "ep005": {"driven_any": 1.2753455667783973, "driven_lanedir": 0.8531196457900734, "in-drivable-lane": 0.9666666666666632, "deviation-heading": 0.4172390868968304, "deviation-center-line": 0.12353370055982248, "driven_lanedir_consec": 0.8531196457900734}, "ep006": {"driven_any": 0.7213718908738914, "driven_lanedir": 0.36298681867932553, "in-drivable-lane": 0.6666666666666687, "deviation-heading": 0.5379976450120086, "deviation-center-line": 0.08770917251999942, "driven_lanedir_consec": 0.36298681867932553}}
165702187wenhuiCopy of #56: sub 1386 by wenhui (ROS-based Lane Following)aido1_LF1_r4-v3step4-vizsuccessno3740:03:30(hidden)
driven_lanedir_consec_median0.013511743990273752
deviation-center-line_median0.1331885549663545
in-drivable-lane_median0.10000000000000032


other stats
deviation-center-line_max1.6399443147871628
deviation-center-line_mean0.5081096720892747
deviation-center-line_min0.06348589900805704
deviation-heading_max1.3881273089231536
deviation-heading_mean0.4977158798090536
deviation-heading_median0.25929939468147384
deviation-heading_min0.15508806450109416
driven_any_max0.17785265394280217
driven_any_mean0.05851344914465829
driven_any_median0.014637578867326232
driven_any_min0.004993333982031001
driven_lanedir_consec_max0.12052166621377036
driven_lanedir_consec_mean0.03759823653910255
driven_lanedir_consec_min0.0046236913832161655
driven_lanedir_max0.12052166621377036
driven_lanedir_mean0.03759823653910255
driven_lanedir_median0.013511743990273752
driven_lanedir_min0.0046236913832161655
in-drivable-lane_max7.73333333333331
in-drivable-lane_mean1.8761904761904704
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.17785265394280217, "driven_lanedir": 0.09364421455687008, "in-drivable-lane": 7.73333333333331, "deviation-heading": 0.8460298219682005, "deviation-center-line": 1.3881209360113882, "driven_lanedir_consec": 0.09364421455687008}, "ep001": {"driven_any": 0.009281822887066657, "driven_lanedir": 0.008820705325031672, "in-drivable-lane": 0, "deviation-heading": 0.22143516247113748, "deviation-center-line": 0.09870928801501264, "driven_lanedir_consec": 0.008820705325031672}, "ep002": {"driven_any": 0.014637578867326232, "driven_lanedir": 0.013511743990273752, "in-drivable-lane": 0.10000000000000032, "deviation-heading": 0.25929939468147384, "deviation-center-line": 0.14694227712968147, "driven_lanedir_consec": 0.013511743990273752}, "ep003": {"driven_any": 0.17784064851693182, "driven_lanedir": 0.12052166621377036, "in-drivable-lane": 5.133333333333315, "deviation-heading": 1.3881273089231536, "deviation-center-line": 1.6399443147871628, "driven_lanedir_consec": 0.12052166621377036}, "ep004": {"driven_any": 0.004993333982031001, "driven_lanedir": 0.0046236913832161655, "in-drivable-lane": 0, "deviation-heading": 0.15508806450109416, "deviation-center-line": 0.06348589900805704, "driven_lanedir_consec": 0.0046236913832161655}, "ep005": {"driven_any": 0.007851496178317116, "driven_lanedir": 0.00748057572596863, "in-drivable-lane": 0, "deviation-heading": 0.18016784151535503, "deviation-center-line": 0.08637643470726605, "driven_lanedir_consec": 0.00748057572596863}, "ep006": {"driven_any": 0.017136609638133068, "driven_lanedir": 0.014585058578587195, "in-drivable-lane": 0.16666666666666718, "deviation-heading": 0.4338635646029605, "deviation-center-line": 0.1331885549663545, "driven_lanedir_consec": 0.014585058578587195}}
165612081DavidCopy of #8: sub 1139 by David (Pytorch IL)aido1_LFV_r2-v3step4-vizsuccessno3740:01:49(hidden)
driven_lanedir_consec_median0.018395828809466847
deviation-center-line_median0.07958923067182998
in-drivable-lane_median0.16666666666666718


other stats
deviation-center-line_max0.3534050425794859
deviation-center-line_mean0.09995818657588688
deviation-center-line_min0
deviation-heading_max0.998330539410922
deviation-heading_mean0.3780174536869544
deviation-heading_median0.24562151165419455
deviation-heading_min0
driven_any_max0.19445128604952563
driven_any_mean0.07746960537215121
driven_any_median0.047274745069806105
driven_any_min0.016675339712618448
driven_lanedir_consec_max0.19003566898539043
driven_lanedir_consec_mean0.04823816450319406
driven_lanedir_consec_min0
driven_lanedir_max0.19003566898539043
driven_lanedir_mean0.048411805057996506
driven_lanedir_median0.01961131269308394
driven_lanedir_min0
in-drivable-lane_max3.1666666666666634
in-drivable-lane_mean0.966666666666665
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.025151946204898027, "driven_lanedir": 0, "in-drivable-lane": 0.9333333333333332, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.19445128604952563, "driven_lanedir": 0.19003566898539043, "in-drivable-lane": 0, "deviation-heading": 0.998330539410922, "deviation-center-line": 0.3534050425794859, "driven_lanedir_consec": 0.19003566898539043}, "ep002": {"driven_any": 0.023093484143266466, "driven_lanedir": 0.01658645409723847, "in-drivable-lane": 0.16666666666666663, "deviation-heading": 0.24562151165419455, "deviation-center-line": 0.031055641849187375, "driven_lanedir_consec": 0.01658645409723847}, "ep003": {"driven_any": 0.016675339712618448, "driven_lanedir": 0.015973111820059, "in-drivable-lane": 0, "deviation-heading": 0.17260445153818085, "deviation-center-line": 0.03599045406297237, "driven_lanedir_consec": 0.015973111820059}, "ep004": {"driven_any": 0.12065163520088244, "driven_lanedir": 0.0553543295951472, "in-drivable-lane": 2.333333333333325, "deviation-heading": 0.23751609643644556, "deviation-center-line": 0.1104402996716508, "driven_lanedir_consec": 0.0553543295951472}, "ep005": {"driven_any": 0.047274745069806105, "driven_lanedir": 0.041321758215056466, "in-drivable-lane": 0.16666666666666718, "deviation-heading": 0.2892831734799989, "deviation-center-line": 0.07958923067182998, "driven_lanedir_consec": 0.041321758215056466}, "ep006": {"driven_any": 0.1149888012240614, "driven_lanedir": 0.01961131269308394, "in-drivable-lane": 3.1666666666666634, "deviation-heading": 0.7027664032889394, "deviation-center-line": 0.08922663719608179, "driven_lanedir_consec": 0.018395828809466847}}
165522081DavidCopy of #8: sub 1139 by David (Pytorch IL)aido1_LFV_r2-v3step3-videossuccessyes3740:01:15(hidden)
other stats
videos1
165412080lavoiemsCopy of #7: sub 819 by lavoiems (Baby Duke)aido1_LFV_r2-v3step1-simulationsuccessyes3740:01:15(hidden)
other stats
simulation-passed1
165312181anna.tsalapovaCopy of #50: sub 396 by anna.tsalapova (simple submition)aido1_LF1_r4-v3step3-videossuccessyes3740:01:15(hidden)
other stats
videos1
165282181anna.tsalapovaCopy of #50: sub 396 by anna.tsalapova (simple submition)aido1_LF1_r4-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": -1.071889101624489, "good_angle": 0.048527729621783845, "survival_time": 16.666666666666654, "traveled_tiles": 3, "valid_direction": 0}, "ep001": {"nsteps": 500, "reward": 0.48687253665924074, "good_angle": 0.07169159269760109, "survival_time": 16.666666666666654, "traveled_tiles": 2, "valid_direction": 0}, "ep002": {"nsteps": 500, "reward": 0.8604411286115646, "good_angle": 0.10274396839687788, "survival_time": 16.666666666666654, "traveled_tiles": 2, "valid_direction": 0}, "ep003": {"nsteps": 500, "reward": 0.4558883606791496, "good_angle": 0.024041380987610977, "survival_time": 16.666666666666654, "traveled_tiles": 3, "valid_direction": 0}, "ep004": {"nsteps": 500, "reward": 0.5293406940102577, "good_angle": 0.029079894290583563, "survival_time": 16.666666666666654, "traveled_tiles": 3, "valid_direction": 0}, "ep005": {"nsteps": 15, "reward": -66.34810793648164, "good_angle": 0.0034065448519183875, "survival_time": 0.49999999999999994, "traveled_tiles": 2, "valid_direction": 0}, "ep006": {"nsteps": 500, "reward": 0.7474124624729156, "good_angle": 0.05015508737956752, "survival_time": 16.666666666666654, "traveled_tiles": 2, "valid_direction": 0}}
good_angle_max0.10274396839687788
good_angle_mean0.04709231403227761
good_angle_median0.048527729621783845
good_angle_min0.0034065448519183875
reward_max0.8604411286115646
reward_mean-9.19143455081043
reward_median0.48687253665924074
reward_min-66.34810793648164
survival_time_max16.666666666666654
survival_time_mean14.357142857142849
survival_time_min0.49999999999999994
traveled_tiles_max3
traveled_tiles_mean2.4285714285714284
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0
valid_direction_mean0
valid_direction_median0
valid_direction_min0
165242188zgxsinCopy of #57: sub 1534 by zgxsin (Random execution)aido1_LF1_r4-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median1.9666666666666697


other stats
episodes
details{"ep000": {"nsteps": 80, "reward": -13.417299686302432, "good_angle": 1.4172995957760004, "survival_time": 2.666666666666667, "traveled_tiles": 2, "valid_direction": 0.966666666666665}, "ep001": {"nsteps": 97, "reward": -10.238907298109178, "good_angle": 0.293293623858769, "survival_time": 3.2333333333333316, "traveled_tiles": 3, "valid_direction": 0.5999999999999979}, "ep002": {"nsteps": 58, "reward": -16.959312186576426, "good_angle": 0.3341123041590959, "survival_time": 1.933333333333336, "traveled_tiles": 2, "valid_direction": 0.7333333333333356}, "ep003": {"nsteps": 59, "reward": -16.658680556578783, "good_angle": 0.03630898015912143, "survival_time": 1.9666666666666697, "traveled_tiles": 2, "valid_direction": 0.06666666666666687}, "ep004": {"nsteps": 58, "reward": -16.9934406295772, "good_angle": 0.0211329915857654, "survival_time": 1.933333333333336, "traveled_tiles": 3, "valid_direction": 0}, "ep005": {"nsteps": 36, "reward": -27.4192532122963, "good_angle": 0.01478750401293466, "survival_time": 1.2000000000000004, "traveled_tiles": 2, "valid_direction": 0}, "ep006": {"nsteps": 66, "reward": -16.030670459523346, "good_angle": 6.589814845235636, "survival_time": 2.200000000000002, "traveled_tiles": 2, "valid_direction": 1.300000000000002}}
good_angle_max6.589814845235636
good_angle_mean1.2438214063981887
good_angle_median0.293293623858769
good_angle_min0.01478750401293466
reward_max-10.238907298109178
reward_mean-16.816794861280524
reward_median-16.658680556578783
reward_min-27.4192532122963
survival_time_max3.2333333333333316
survival_time_mean2.1619047619047636
survival_time_min1.2000000000000004
traveled_tiles_max3
traveled_tiles_mean2.2857142857142856
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max1.300000000000002
valid_direction_mean0.523809523809524
valid_direction_median0.5999999999999979
valid_direction_min0
165152188zgxsinCopy of #57: sub 1534 by zgxsin (Random execution)aido1_LF1_r4-v3step1-simulationsuccessyes3740:01:27(hidden)
other stats
simulation-passed1
165012177AmaurXCopy of #46: sub 780 by AmaurX (AMOD18-AIDO not that random execution)aido1_LF1_r4-v3step3-videossuccessyes3740:00:42(hidden)
other stats
videos1
164862177AmaurXCopy of #46: sub 780 by AmaurX (AMOD18-AIDO not that random execution)aido1_LF1_r4-v3step2-scoringsuccessyes3740:01:40(hidden)
survival_time_median1.2666666666666673


other stats
episodes
details{"ep000": {"nsteps": 69, "reward": -15.531301944800044, "good_angle": 0.9498973991995284, "survival_time": 2.3000000000000016, "traveled_tiles": 3, "valid_direction": 1.400000000000001}, "ep001": {"nsteps": 13, "reward": -76.70099361054599, "good_angle": 0.12120442370327224, "survival_time": 0.4333333333333333, "traveled_tiles": 2, "valid_direction": 0.2333333333333333}, "ep002": {"nsteps": 26, "reward": -37.957806984105936, "good_angle": 0.1801107972321388, "survival_time": 0.8666666666666666, "traveled_tiles": 2, "valid_direction": 0.36666666666666664}, "ep003": {"nsteps": 38, "reward": -26.94640799366722, "good_angle": 0.7075325028158037, "survival_time": 1.2666666666666673, "traveled_tiles": 1, "valid_direction": 1.066666666666667}, "ep004": {"nsteps": 65, "reward": -14.76429815177734, "good_angle": 0.336056800236373, "survival_time": 2.1666666666666687, "traveled_tiles": 3, "valid_direction": 0.7666666666666673}, "ep005": {"nsteps": 15, "reward": -66.35287038137515, "good_angle": 0.04720639041177621, "survival_time": 0.49999999999999994, "traveled_tiles": 1, "valid_direction": 0.1333333333333333}, "ep006": {"nsteps": 48, "reward": -22.422542830308277, "good_angle": 4.181314648030019, "survival_time": 1.6000000000000016, "traveled_tiles": 2, "valid_direction": 1.1000000000000016}}
good_angle_max4.181314648030019
good_angle_mean0.9319032802327016
good_angle_median0.336056800236373
good_angle_min0.04720639041177621
reward_max-14.76429815177734
reward_mean-37.23946027093999
reward_median-26.94640799366722
reward_min-76.70099361054599
survival_time_max2.3000000000000016
survival_time_mean1.3047619047619057
survival_time_min0.4333333333333333
traveled_tiles_max3
traveled_tiles_mean2
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max1.400000000000001
valid_direction_mean0.7238095238095245
valid_direction_median0.7666666666666673
valid_direction_min0.1333333333333333
164642173kbensonCopy of #42: sub 769 by kbenson (AMOD18-AIDO not that random execution)aido1_LF1_r4-v3step1-simulationsuccessyes3740:01:10(hidden)
other stats
simulation-passed1
164512163iban_alexCopy of #32: sub 651 by iban_alex (PyTorch template)aido1_LF1_r4-v3step4-vizsuccessno3740:01:39(hidden)
driven_lanedir_consec_median0.02366237082346804
deviation-center-line_median0.1287994364475442
in-drivable-lane_median0.8000000000000016


other stats
deviation-center-line_max0.2629881175237683
deviation-center-line_mean0.11453905768470352
deviation-center-line_min0
deviation-heading_max1.6675137110076186
deviation-heading_mean0.6245372852361745
deviation-heading_median0.515581102447769
deviation-heading_min0
driven_any_max0.10732501554040404
driven_any_mean0.04418653409615993
driven_any_median0.03300519489708848
driven_any_min0.01702402654321302
driven_lanedir_consec_max0.048993945816521944
driven_lanedir_consec_mean0.023022827594289353
driven_lanedir_consec_min0
driven_lanedir_max0.048993945816521944
driven_lanedir_mean0.023022827594289353
driven_lanedir_median0.02366237082346804
driven_lanedir_min0
in-drivable-lane_max3.233333333333327
in-drivable-lane_mean1.1380952380952385
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.01702402654321302, "driven_lanedir": 0, "in-drivable-lane": 0.7999999999999999, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.027666503602141207, "driven_lanedir": 0.014505812055083316, "in-drivable-lane": 0.6666666666666672, "deviation-heading": 0.16656232373317398, "deviation-center-line": 0.03662018719325536, "driven_lanedir_consec": 0.014505812055083316}, "ep002": {"driven_any": 0.03075632895661315, "driven_lanedir": 0.02868846499794423, "in-drivable-lane": 0, "deviation-heading": 0.515581102447769, "deviation-center-line": 0.1287994364475442, "driven_lanedir_consec": 0.02868846499794423}, "ep003": {"driven_any": 0.04957732686566565, "driven_lanedir": 0.02366237082346804, "in-drivable-lane": 1.2333333333333356, "deviation-heading": 0.9246204263612922, "deviation-center-line": 0.154875071880847, "driven_lanedir_consec": 0.02366237082346804}, "ep004": {"driven_any": 0.04395134226799394, "driven_lanedir": 0.02969404748098949, "in-drivable-lane": 0.8000000000000016, "deviation-heading": 0.8567541398966259, "deviation-center-line": 0.17102506648466692, "driven_lanedir_consec": 0.02969404748098949}, "ep005": {"driven_any": 0.10732501554040404, "driven_lanedir": 0.048993945816521944, "in-drivable-lane": 3.233333333333327, "deviation-heading": 1.6675137110076186, "deviation-center-line": 0.2629881175237683, "driven_lanedir_consec": 0.048993945816521944}, "ep006": {"driven_any": 0.03300519489708848, "driven_lanedir": 0.015615151986018446, "in-drivable-lane": 1.2333333333333358, "deviation-heading": 0.24072929320674152, "deviation-center-line": 0.047465524262842895, "driven_lanedir_consec": 0.015615151986018446}}
164412163iban_alexCopy of #32: sub 651 by iban_alex (PyTorch template)aido1_LF1_r4-v3step3-videossuccessyes3740:00:49(hidden)
other stats
videos1
164162159zjdongCopy of #28: sub 782 by zjdong (AMOD18-AIDO not that random execution)aido1_LF1_r4-v3step4-vizsuccessno3740:04:04(hidden)
driven_lanedir_consec_median0.025005918395635054
deviation-center-line_median0.14843401158372518
in-drivable-lane_median0.4666666666666681


other stats
deviation-center-line_max0.2808888022028034
deviation-center-line_mean0.15481162863124578
deviation-center-line_min0.06113231489334276
deviation-heading_max0.7105409203253105
deviation-heading_mean0.3418269807296938
deviation-heading_median0.2780730356419934
deviation-heading_min0.13140730150841332
driven_any_max0.0443497314815339
driven_any_mean0.03365264748711383
driven_any_median0.03110340675795758
driven_any_min0.026053154750729772
driven_lanedir_consec_max0.03529759900132664
driven_lanedir_consec_mean0.02361631311895302
driven_lanedir_consec_min0.003782123096066582
driven_lanedir_max0.03529759900132664
driven_lanedir_mean0.02361631311895302
driven_lanedir_median0.025005918395635054
driven_lanedir_min0.003782123096066582
in-drivable-lane_max2.033333333333336
in-drivable-lane_mean0.6571428571428574
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.03661378922629066, "driven_lanedir": 0.003782123096066582, "in-drivable-lane": 2.033333333333336, "deviation-heading": 0.7105409203253105, "deviation-center-line": 0.06113231489334276, "driven_lanedir_consec": 0.003782123096066582}, "ep001": {"driven_any": 0.0443497314815339, "driven_lanedir": 0.03529759900132664, "in-drivable-lane": 0.6666666666666643, "deviation-heading": 0.16082379093815466, "deviation-center-line": 0.2674105952180784, "driven_lanedir_consec": 0.03529759900132664}, "ep002": {"driven_any": 0.026053154750729772, "driven_lanedir": 0.019084363729448828, "in-drivable-lane": 0.4666666666666681, "deviation-heading": 0.2780730356419934, "deviation-center-line": 0.08198636305858252, "driven_lanedir_consec": 0.019084363729448828}, "ep003": {"driven_any": 0.02815283167505222, "driven_lanedir": 0.025005918395635054, "in-drivable-lane": 0.2333333333333334, "deviation-heading": 0.16067998591599073, "deviation-center-line": 0.17883877338731105, "driven_lanedir_consec": 0.025005918395635054}, "ep004": {"driven_any": 0.03938042989675295, "driven_lanedir": 0.03505060145138934, "in-drivable-lane": 0.2333333333333325, "deviation-heading": 0.5914111454084457, "deviation-center-line": 0.14843401158372518, "driven_lanedir_consec": 0.03505060145138934}, "ep005": {"driven_any": 0.029915188621479703, "driven_lanedir": 0.02984316572767775, "in-drivable-lane": 0, "deviation-heading": 0.13140730150841332, "deviation-center-line": 0.2808888022028034, "driven_lanedir_consec": 0.02984316572767775}, "ep006": {"driven_any": 0.03110340675795758, "driven_lanedir": 0.017250420431126942, "in-drivable-lane": 0.9666666666666676, "deviation-heading": 0.3598526853695485, "deviation-center-line": 0.06499054007487708, "driven_lanedir_consec": 0.017250420431126942}}
164102158jahanviCopy of #27: sub 422 by jahanvi (Random execution)aido1_LF1_r4-v3step3-videossuccessyes3740:00:42(hidden)
other stats
videos1
163922156krishnaCopy of #25: sub 1143 by krishna (gym_duckietown + opencv)aido1_LF1_r4-v3step1-simulationsuccessyes3740:02:12(hidden)
other stats
simulation-passed1
163592140orlando.m.bol@gmail.comCopy of #9: sub 436 by orlando.m.bol@gmail.com (PyTorch DDPG template)aido1_LF1_r4-v3step4-vizsuccessno3740:08:24(hidden)
driven_lanedir_consec_median0.15536575339365455
deviation-center-line_median1.0471529978202343
in-drivable-lane_median1.699999999999997


other stats
deviation-center-line_max1.1490350613988942
deviation-center-line_mean1.0504708003120018
deviation-center-line_min0.93658483864408
deviation-heading_max10.856514258870236
deviation-heading_mean10.28519658029301
deviation-heading_median10.63118630323542
deviation-heading_min8.821929989783744
driven_any_max0.4972026132196988
driven_any_mean0.4972005912314144
driven_any_median0.49720079695780794
driven_any_min0.49719755378072966
driven_lanedir_consec_max0.15690365060897174
driven_lanedir_consec_mean0.14894408901737174
driven_lanedir_consec_min0.13774865622426474
driven_lanedir_max0.3117956118561568
driven_lanedir_mean0.3037826670759708
driven_lanedir_median0.3071329272656625
driven_lanedir_min0.28665067006446987
in-drivable-lane_max3.2666666666666604
in-drivable-lane_mean1.9904761904761872
in-drivable-lane_min1.466666666666664
per-episodes
details{"ep000": {"driven_any": 0.49720079695780794, "driven_lanedir": 0.3083097092583809, "in-drivable-lane": 1.5666666666666638, "deviation-heading": 10.806032286975588, "deviation-center-line": 1.0460345287563428, "driven_lanedir_consec": 0.15690365060897174}, "ep001": {"driven_any": 0.4971977244784423, "driven_lanedir": 0.30987975017019626, "in-drivable-lane": 1.4999999999999971, "deviation-heading": 10.856514258870236, "deviation-center-line": 0.973351038594387, "driven_lanedir_consec": 0.15561997272658093}, "ep002": {"driven_any": 0.4972026132196971, "driven_lanedir": 0.30462796119202573, "in-drivable-lane": 1.9666666666666648, "deviation-heading": 10.30581734168244, "deviation-center-line": 1.0471529978202343, "driven_lanedir_consec": 0.13971506567694117}, "ep003": {"driven_any": 0.4972026132196984, "driven_lanedir": 0.29808203972490355, "in-drivable-lane": 2.4666666666666632, "deviation-heading": 9.735558515644255, "deviation-center-line": 1.1490350613988942, "driven_lanedir_consec": 0.13774865622426474}, "ep004": {"driven_any": 0.4972026132196988, "driven_lanedir": 0.3071329272656625, "in-drivable-lane": 1.699999999999997, "deviation-heading": 10.63118630323542, "deviation-center-line": 1.0605488751185932, "driven_lanedir_consec": 0.15684467104568278}, "ep005": {"driven_any": 0.49719755378072966, "driven_lanedir": 0.3117956118561568, "in-drivable-lane": 1.466666666666664, "deviation-heading": 10.839337365859397, "deviation-center-line": 0.93658483864408, "driven_lanedir_consec": 0.15536575339365455}, "ep006": {"driven_any": 0.49720022374382655, "driven_lanedir": 0.28665067006446987, "in-drivable-lane": 3.2666666666666604, "deviation-heading": 8.821929989783744, "deviation-center-line": 1.1405882618514804, "driven_lanedir_consec": 0.14041085344550652}}
163572142hvrigazovCopy of #11: sub 1552 by hvrigazov (Visteon perception team)aido1_LF1_r4-v3step2-scoringsuccessyes3740:00:25(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.3729007467478514, "good_angle": 8.16140654744899, "survival_time": 16.666666666666654, "traveled_tiles": 9, "valid_direction": 3.866666666666678}, "ep001": {"nsteps": 500, "reward": 0.5899814558625222, "good_angle": 0.889829858177259, "survival_time": 16.666666666666654, "traveled_tiles": 13, "valid_direction": 1.6666666666666643}, "ep002": {"nsteps": 500, "reward": 0.6537864317297936, "good_angle": 0.7036828774301513, "survival_time": 16.666666666666654, "traveled_tiles": 13, "valid_direction": 1.2666666666666653}, "ep003": {"nsteps": 500, "reward": 0.46342139748157934, "good_angle": 2.385170778954968, "survival_time": 16.666666666666654, "traveled_tiles": 8, "valid_direction": 3.4999999999999907}, "ep004": {"nsteps": 135, "reward": -7.479596372666182, "good_angle": 5.147318534481444, "survival_time": 4.499999999999994, "traveled_tiles": 3, "valid_direction": 1.999999999999994}, "ep005": {"nsteps": 220, "reward": -4.536541949678212, "good_angle": 5.98717359125595, "survival_time": 7.333333333333317, "traveled_tiles": 3, "valid_direction": 3.933333333333327}, "ep006": {"nsteps": 37, "reward": -27.11311831749422, "good_angle": 0.49311495557958673, "survival_time": 1.2333333333333338, "traveled_tiles": 1, "valid_direction": 0.6333333333333337}}
good_angle_max8.16140654744899
good_angle_mean3.3953853061897648
good_angle_median2.385170778954968
good_angle_min0.49311495557958673
reward_max0.6537864317297936
reward_mean-5.292738086859552
reward_median0.3729007467478514
reward_min-27.11311831749422
survival_time_max16.666666666666654
survival_time_mean11.39047619047618
survival_time_min1.2333333333333338
traveled_tiles_max13
traveled_tiles_mean7.142857142857143
traveled_tiles_median8
traveled_tiles_min1
valid_direction_max3.933333333333327
valid_direction_mean2.4095238095238076
valid_direction_median1.999999999999994
valid_direction_min0.6333333333333337
163382142hvrigazovCopy of #11: sub 1552 by hvrigazov (Visteon perception team)aido1_LF1_r4-v3step1-simulationsuccessyes3740:03:35(hidden)
other stats
simulation-passed1
163262133heyt0nyCopy of #2: sub 1579 by heyt0ny (SAIC MOSCOW MML)aido1_LF1_r4-v3step2-scoringsuccessyes3740:03:15(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.9662138417959212, "good_angle": 0.4167309446437648, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.39999999999999947}, "ep001": {"nsteps": 500, "reward": 1.0747047505378724, "good_angle": 0.07193785363582643, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0}, "ep002": {"nsteps": 500, "reward": 0.9995707498788834, "good_angle": 0.136117579772428, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.033333333333333215}, "ep003": {"nsteps": 500, "reward": 0.915099334731698, "good_angle": 0.25205031327806365, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.19999999999999973}, "ep004": {"nsteps": 500, "reward": 0.974284889638424, "good_angle": 0.181073089687222, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.03333333333333344}, "ep005": {"nsteps": 500, "reward": 0.9715192890390754, "good_angle": 0.24254321790355657, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.26666666666666705}, "ep006": {"nsteps": 500, "reward": 0.6436662563085556, "good_angle": 29.160514560999133, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 3.1999999999999957}}
good_angle_max29.160514560999133
good_angle_mean4.351566794274285
good_angle_median0.24254321790355657
good_angle_min0.07193785363582643
reward_max1.0747047505378724
reward_mean0.93500844456149
reward_median0.9715192890390754
reward_min0.6436662563085556
survival_time_max16.666666666666654
survival_time_mean16.666666666666654
survival_time_min16.666666666666654
traveled_tiles_max18
traveled_tiles_mean18
traveled_tiles_median18
traveled_tiles_min18
valid_direction_max3.1999999999999957
valid_direction_mean0.5904761904761898
valid_direction_median0.19999999999999973
valid_direction_min0
163132129wenhuiCopy of #56: sub 1386 by wenhui (ROS-based Lane Following)aido1_LFV_r2-v3step4-vizsuccessno3740:03:24(hidden)
driven_lanedir_consec_median0.007312827225401541
deviation-center-line_median0.07712712513937398
in-drivable-lane_median0


other stats
deviation-center-line_max0.17494040140379025
deviation-center-line_mean0.07958932560145339
deviation-center-line_min0
deviation-heading_max0.2559508517537955
deviation-heading_mean0.15239730007229232
deviation-heading_median0.14662409243271646
deviation-heading_min0
driven_any_max0.016780253883363606
driven_any_mean0.009739704505573394
driven_any_median0.00856478454672888
driven_any_min0.00570983589735583
driven_lanedir_consec_max0.016086641785682983
driven_lanedir_consec_mean0.007452340744501066
driven_lanedir_consec_min0
driven_lanedir_max0.016086641785682983
driven_lanedir_mean0.007452340744501066
driven_lanedir_median0.007312827225401541
driven_lanedir_min0
in-drivable-lane_max1.3000000000000007
in-drivable-lane_mean0.20000000000000012
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.013564689132408053, "driven_lanedir": 0, "in-drivable-lane": 1.3000000000000007, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.008927322408545749, "driven_lanedir": 0.008821712965623736, "in-drivable-lane": 0, "deviation-heading": 0.12585791283290512, "deviation-center-line": 0.06616467246028196, "driven_lanedir_consec": 0.008821712965623736}, "ep002": {"driven_any": 0.007133382608686966, "driven_lanedir": 0.006269905813547449, "in-drivable-lane": 0.033333333333333326, "deviation-heading": 0.2559508517537955, "deviation-center-line": 0.07712712513937398, "driven_lanedir_consec": 0.006269905813547449}, "ep003": {"driven_any": 0.007497663061924676, "driven_lanedir": 0.007312827225401541, "in-drivable-lane": 0, "deviation-heading": 0.14662409243271646, "deviation-center-line": 0.06529256869265809, "driven_lanedir_consec": 0.007312827225401541}, "ep004": {"driven_any": 0.00570983589735583, "driven_lanedir": 0.005584895617729849, "in-drivable-lane": 0, "deviation-heading": 0.1061221029041818, "deviation-center-line": 0.0790523821338049, "driven_lanedir_consec": 0.005584895617729849}, "ep005": {"driven_any": 0.016780253883363606, "driven_lanedir": 0.016086641785682983, "in-drivable-lane": 0.06666666666666687, "deviation-heading": 0.21818384243084704, "deviation-center-line": 0.17494040140379025, "driven_lanedir_consec": 0.016086641785682983}, "ep006": {"driven_any": 0.00856478454672888, "driven_lanedir": 0.008090401803521895, "in-drivable-lane": 0, "deviation-heading": 0.21404229815160028, "deviation-center-line": 0.09454812938026462, "driven_lanedir_consec": 0.008090401803521895}}
162972130zgxsinCopy of #57: sub 1534 by zgxsin (Random execution)aido1_LFV_r2-v3step2-scoringsuccessyes3740:01:46(hidden)
survival_time_median2.4000000000000012


other stats
episodes
details{"ep000": {"nsteps": 88, "reward": -12.62165555222468, "good_angle": 1.4937495150728863, "survival_time": 2.9333333333333327, "traveled_tiles": 3, "valid_direction": 0.9999999999999968}, "ep001": {"nsteps": 63, "reward": -17.43409714433882, "good_angle": 5.895456133631279, "survival_time": 2.1000000000000023, "traveled_tiles": 2, "valid_direction": 1.3000000000000025}, "ep002": {"nsteps": 72, "reward": -15.344475728770098, "good_angle": 6.1585852464362425, "survival_time": 2.4000000000000012, "traveled_tiles": 2, "valid_direction": 1.366666666666668}, "ep003": {"nsteps": 35, "reward": -29.095867948446955, "good_angle": 0.001267973201332321, "survival_time": 1.166666666666667, "traveled_tiles": 2, "valid_direction": 0}, "ep004": {"nsteps": 116, "reward": -9.4097869417034, "good_angle": 8.016852348841546, "survival_time": 3.8666666666666623, "traveled_tiles": 3, "valid_direction": 1.3666666666666618}, "ep005": {"nsteps": 179, "reward": -5.69365241157942, "good_angle": 5.9944784265088735, "survival_time": 5.966666666666655, "traveled_tiles": 5, "valid_direction": 1.3666666666666618}, "ep006": {"nsteps": 21, "reward": -49.70975196006752, "good_angle": 0.004179848103009616, "survival_time": 0.7, "traveled_tiles": 2, "valid_direction": 0}}
good_angle_max8.016852348841546
good_angle_mean3.937795641685024
good_angle_median5.895456133631279
good_angle_min0.001267973201332321
reward_max-5.69365241157942
reward_mean-19.901326812447273
reward_median-15.344475728770098
reward_min-49.70975196006752
survival_time_max5.966666666666655
survival_time_mean2.733333333333331
survival_time_min0.7
traveled_tiles_max5
traveled_tiles_mean2.7142857142857144
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max1.366666666666668
valid_direction_mean0.9142857142857128
valid_direction_median1.3000000000000025
valid_direction_min0
162872120sischaefCopy of #47: sub 770 by sischaef (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step3-videossuccessyes3740:00:45(hidden)
other stats
videos1
162742120sischaefCopy of #47: sub 770 by sischaef (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step2-scoringsuccessyes3740:01:39(hidden)
survival_time_median1.466666666666668


other stats
episodes
details{"ep000": {"nsteps": 62, "reward": -17.368721367460825, "good_angle": 1.0243686406506889, "survival_time": 2.066666666666669, "traveled_tiles": 3, "valid_direction": 0.733333333333335}, "ep001": {"nsteps": 45, "reward": -23.595851649302578, "good_angle": 4.182889478452886, "survival_time": 1.500000000000001, "traveled_tiles": 2, "valid_direction": 0.9333333333333348}, "ep002": {"nsteps": 12, "reward": -85.85660953323047, "good_angle": 0.0019887542141695503, "survival_time": 0.4, "traveled_tiles": 2, "valid_direction": 0}, "ep003": {"nsteps": 20, "reward": -55.01876010596752, "good_angle": 0.0015869330545103374, "survival_time": 0.6666666666666666, "traveled_tiles": 2, "valid_direction": 0}, "ep004": {"nsteps": 44, "reward": -23.766158009794623, "good_angle": 3.798391071754617, "survival_time": 1.466666666666668, "traveled_tiles": 2, "valid_direction": 0.9333333333333346}, "ep005": {"nsteps": 31, "reward": -31.98308232739087, "good_angle": 0.33065465258981025, "survival_time": 1.0333333333333332, "traveled_tiles": 2, "valid_direction": 0.033333333333333326}, "ep006": {"nsteps": 79, "reward": -12.930871123000037, "good_angle": 4.972413571850335, "survival_time": 2.6333333333333337, "traveled_tiles": 3, "valid_direction": 0.9333333333333318}}
good_angle_max4.972413571850335
good_angle_mean2.0446133003667164
good_angle_median1.0243686406506889
good_angle_min0.0015869330545103374
reward_max-12.930871123000037
reward_mean-35.788579159449554
reward_median-23.766158009794623
reward_min-85.85660953323047
survival_time_max2.6333333333333337
survival_time_mean1.395238095238096
survival_time_min0.4
traveled_tiles_max3
traveled_tiles_mean2.2857142857142856
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0.9333333333333348
valid_direction_mean0.5095238095238098
valid_direction_median0.733333333333335
valid_direction_min0
162562117licCopy of #44: sub 783 by lic (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step2-scoringsuccessyes3740:01:37(hidden)
survival_time_median0.7999999999999999


other stats
episodes
details{"ep000": {"nsteps": 31, "reward": -33.47593748803821, "good_angle": 0.5271514565643505, "survival_time": 1.0333333333333332, "traveled_tiles": 3, "valid_direction": 0.3666666666666666}, "ep001": {"nsteps": 42, "reward": -25.59829720212812, "good_angle": 2.4620996794441967, "survival_time": 1.400000000000001, "traveled_tiles": 3, "valid_direction": 0.5000000000000011}, "ep002": {"nsteps": 12, "reward": -83.64394994576772, "good_angle": 0.0006869739755386177, "survival_time": 0.4, "traveled_tiles": 2, "valid_direction": 0}, "ep003": {"nsteps": 24, "reward": -42.14271882737133, "good_angle": 0.1449913009346389, "survival_time": 0.7999999999999999, "traveled_tiles": 2, "valid_direction": 0.3}, "ep004": {"nsteps": 20, "reward": -50.22657583958935, "good_angle": 0.1343777461396107, "survival_time": 0.6666666666666666, "traveled_tiles": 2, "valid_direction": 0.3}, "ep005": {"nsteps": 40, "reward": -25.314252318441863, "good_angle": 2.6482210832773307, "survival_time": 1.333333333333334, "traveled_tiles": 3, "valid_direction": 0.5000000000000009}, "ep006": {"nsteps": 23, "reward": -43.17621100467184, "good_angle": 0.001571058874392761, "survival_time": 0.7666666666666666, "traveled_tiles": 3, "valid_direction": 0}}
good_angle_max2.6482210832773307
good_angle_mean0.8455856141728655
good_angle_median0.1449913009346389
good_angle_min0.0006869739755386177
reward_max-25.314252318441863
reward_mean-43.3682775180012
reward_median-42.14271882737133
reward_min-83.64394994576772
survival_time_max1.400000000000001
survival_time_mean0.9142857142857144
survival_time_min0.4
traveled_tiles_max3
traveled_tiles_mean2.5714285714285716
traveled_tiles_median3
traveled_tiles_min2
valid_direction_max0.5000000000000011
valid_direction_mean0.28095238095238123
valid_direction_median0.3
valid_direction_min0
162242106martatintoreCopy of #33: sub 786 by martatintore (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step4-vizsuccessno3740:04:06(hidden)
driven_lanedir_consec_median0.01759797175702954
deviation-center-line_median0.1218470405215174
in-drivable-lane_median0.6666666666666674


other stats
deviation-center-line_max0.3070674866597604
deviation-center-line_mean0.14201640370773308
deviation-center-line_min0.04627062009108976
deviation-heading_max0.7336265499896395
deviation-heading_mean0.41289971036982426
deviation-heading_median0.47206426169382665
deviation-heading_min0.03661968047836495
driven_any_max0.08196181024981197
driven_any_mean0.03719931075973136
driven_any_median0.03278478464869382
driven_any_min0.00889424209042909
driven_lanedir_consec_max0.06794769366630118
driven_lanedir_consec_mean0.02473661173964833
driven_lanedir_consec_min0.002137007342325255
driven_lanedir_max0.06794769366630118
driven_lanedir_mean0.02473661173964833
driven_lanedir_median0.01759797175702954
driven_lanedir_min0.002137007342325255
in-drivable-lane_max2.366666666666668
in-drivable-lane_mean0.7666666666666659
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.04048187217111784, "driven_lanedir": 0.002137007342325255, "in-drivable-lane": 2.366666666666668, "deviation-heading": 0.7336265499896395, "deviation-center-line": 0.05280390694687345, "driven_lanedir_consec": 0.002137007342325255}, "ep001": {"driven_any": 0.02930499839991531, "driven_lanedir": 0.01759797175702954, "in-drivable-lane": 0.6666666666666674, "deviation-heading": 0.5268488219522447, "deviation-center-line": 0.113655923547413, "driven_lanedir_consec": 0.01759797175702954}, "ep002": {"driven_any": 0.03278478464869382, "driven_lanedir": 0.02105605906440403, "in-drivable-lane": 0.6333333333333324, "deviation-heading": 0.5917969755931286, "deviation-center-line": 0.14328139278754287, "driven_lanedir_consec": 0.02105605906440403}, "ep003": {"driven_any": 0.015204958639517516, "driven_lanedir": 0.015193045656468947, "in-drivable-lane": 0, "deviation-heading": 0.03661968047836495, "deviation-center-line": 0.1218470405215174, "driven_lanedir_consec": 0.015193045656468947}, "ep004": {"driven_any": 0.05176250911863392, "driven_lanedir": 0.04037822660988442, "in-drivable-lane": 0.6999999999999975, "deviation-heading": 0.47206426169382665, "deviation-center-line": 0.2091884553999348, "driven_lanedir_consec": 0.04037822660988442}, "ep005": {"driven_any": 0.08196181024981197, "driven_lanedir": 0.06794769366630118, "in-drivable-lane": 0.9999999999999964, "deviation-heading": 0.462068209276685, "deviation-center-line": 0.3070674866597604, "driven_lanedir_consec": 0.06794769366630118}, "ep006": {"driven_any": 0.00889424209042909, "driven_lanedir": 0.008846278081124924, "in-drivable-lane": 0, "deviation-heading": 0.06727347360487974, "deviation-center-line": 0.04627062009108976, "driven_lanedir_consec": 0.008846278081124924}}
162072102patripfrCopy of #29: sub 872 by patripfr (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step4-vizsuccessno3740:01:40(hidden)
driven_lanedir_consec_median0.017073340721759613
deviation-center-line_median0.12506162434793316
in-drivable-lane_median0.6333333333333311


other stats
deviation-center-line_max0.4955841556129441
deviation-center-line_mean0.16983782907468645
deviation-center-line_min0.060216219759902
deviation-heading_max0.724912268434709
deviation-heading_mean0.4217483616273355
deviation-heading_median0.48179337152812984
deviation-heading_min0.04965750037369722
driven_any_max0.05522067448661374
driven_any_mean0.03397548337336328
driven_any_median0.03207934397424492
driven_any_min0.01385514211785329
driven_lanedir_consec_max0.055083632875241866
driven_lanedir_consec_mean0.02380045519413446
driven_lanedir_consec_min0.004039923162869686
driven_lanedir_max0.055083632875241866
driven_lanedir_mean0.02380045519413446
driven_lanedir_median0.017073340721759613
driven_lanedir_min0.004039923162869686
in-drivable-lane_max2.2666666666666684
in-drivable-lane_mean0.5999999999999999
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.04019552715773436, "driven_lanedir": 0.004039923162869686, "in-drivable-lane": 2.2666666666666684, "deviation-heading": 0.6925826189645483, "deviation-center-line": 0.060216219759902, "driven_lanedir_consec": 0.004039923162869686}, "ep001": {"driven_any": 0.028979613261754723, "driven_lanedir": 0.017073340721759613, "in-drivable-lane": 0.633333333333334, "deviation-heading": 0.6517551183622916, "deviation-center-line": 0.1315355586946579, "driven_lanedir_consec": 0.017073340721759613}, "ep002": {"driven_any": 0.03207934397424492, "driven_lanedir": 0.02054808185712722, "in-drivable-lane": 0.6666666666666663, "deviation-heading": 0.48179337152812984, "deviation-center-line": 0.12506162434793316, "driven_lanedir_consec": 0.02054808185712722}, "ep003": {"driven_any": 0.015227987214397084, "driven_lanedir": 0.01520717648321674, "in-drivable-lane": 0, "deviation-heading": 0.04965750037369722, "deviation-center-line": 0.12277953913777558, "driven_lanedir_consec": 0.01520717648321674}, "ep004": {"driven_any": 0.05227009540094485, "driven_lanedir": 0.04088351920473341, "in-drivable-lane": 0.6333333333333311, "deviation-heading": 0.724912268434709, "deviation-center-line": 0.13350925166163333, "driven_lanedir_consec": 0.04088351920473341}, "ep005": {"driven_any": 0.05522067448661374, "driven_lanedir": 0.055083632875241866, "in-drivable-lane": 0, "deviation-heading": 0.2424637658260523, "deviation-center-line": 0.4955841556129441, "driven_lanedir_consec": 0.055083632875241866}, "ep006": {"driven_any": 0.01385514211785329, "driven_lanedir": 0.01376751205399267, "in-drivable-lane": 0, "deviation-heading": 0.1090738879019199, "deviation-center-line": 0.12017845430795912, "driven_lanedir_consec": 0.01376751205399267}}
161972102patripfrCopy of #29: sub 872 by patripfr (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step3-videossuccessyes3740:00:46(hidden)
other stats
videos1
161922102patripfrCopy of #29: sub 872 by patripfr (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median2.333333333333335


other stats
episodes
details{"ep000": {"nsteps": 88, "reward": -12.630915343729695, "good_angle": 1.3346600653451737, "survival_time": 2.9333333333333327, "traveled_tiles": 3, "valid_direction": 0.9999999999999968}, "ep001": {"nsteps": 65, "reward": -16.54905170935851, "good_angle": 5.579573620046505, "survival_time": 2.1666666666666687, "traveled_tiles": 2, "valid_direction": 1.3333333333333357}, "ep002": {"nsteps": 70, "reward": -15.937452033587864, "good_angle": 5.876315462502251, "survival_time": 2.333333333333335, "traveled_tiles": 2, "valid_direction": 1.3000000000000016}, "ep003": {"nsteps": 34, "reward": -29.865733626134254, "good_angle": 0.0029461863277830164, "survival_time": 1.1333333333333335, "traveled_tiles": 2, "valid_direction": 0}, "ep004": {"nsteps": 114, "reward": -8.884856242098307, "good_angle": 6.441641134602666, "survival_time": 3.799999999999996, "traveled_tiles": 3, "valid_direction": 1.3333333333333286}, "ep005": {"nsteps": 121, "reward": -7.947933464749785, "good_angle": 0.021162941427438277, "survival_time": 4.033333333333329, "traveled_tiles": 4, "valid_direction": 0}, "ep006": {"nsteps": 31, "reward": -31.931666533552832, "good_angle": 0.013196717792324668, "survival_time": 1.0333333333333332, "traveled_tiles": 1, "valid_direction": 0}}
good_angle_max6.441641134602666
good_angle_mean2.752785161149163
good_angle_median1.3346600653451737
good_angle_min0.0029461863277830164
reward_max-7.947933464749785
reward_mean-17.67822985045875
reward_median-15.937452033587864
reward_min-31.931666533552832
survival_time_max4.033333333333329
survival_time_mean2.49047619047619
survival_time_min1.0333333333333332
traveled_tiles_max4
traveled_tiles_mean2.4285714285714284
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max1.3333333333333357
valid_direction_mean0.7095238095238089
valid_direction_median0.9999999999999968
valid_direction_min0
161772102patripfrCopy of #29: sub 872 by patripfr (AMOD18-AIDO not that random execution)aido1_LFV_r2-v3step1-simulationsuccessyes3740:01:32(hidden)
other stats
simulation-passed1
161702097MandanaSamieiCopy of #24: sub 687 by MandanaSamiei (ROS-based Lane Following)aido1_LFV_r2-v3step3-videossuccessyes3740:00:49(hidden)
other stats
videos1
161652097MandanaSamieiCopy of #24: sub 687 by MandanaSamiei (ROS-based Lane Following)aido1_LFV_r2-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median1.0333333333333332


other stats
episodes
details{"ep000": {"nsteps": 39, "reward": -27.214298458435596, "good_angle": 0.20450446928452704, "survival_time": 1.3000000000000007, "traveled_tiles": 2, "valid_direction": 0.4666666666666666}, "ep001": {"nsteps": 31, "reward": -36.00659805776611, "good_angle": 0.06393236592622958, "survival_time": 1.0333333333333332, "traveled_tiles": 1, "valid_direction": 0.23333333333333328}, "ep002": {"nsteps": 21, "reward": -50.040869912930894, "good_angle": 0.03210879995387549, "survival_time": 0.7, "traveled_tiles": 2, "valid_direction": 0.1333333333333333}, "ep003": {"nsteps": 30, "reward": -38.44008622517188, "good_angle": 0.1558937988628871, "survival_time": 1, "traveled_tiles": 2, "valid_direction": 0.49999999999999994}, "ep004": {"nsteps": 45, "reward": -22.462565561466747, "good_angle": 1.5735909324701436, "survival_time": 1.500000000000001, "traveled_tiles": 3, "valid_direction": 0.600000000000001}, "ep005": {"nsteps": 13, "reward": -76.76320248441054, "good_angle": 0.19327267307552995, "survival_time": 0.4333333333333333, "traveled_tiles": 1, "valid_direction": 0.2}, "ep006": {"nsteps": 115, "reward": -8.485343547887169, "good_angle": 5.934118795423771, "survival_time": 3.8333333333333295, "traveled_tiles": 4, "valid_direction": 1.4333333333333294}}
good_angle_max5.934118795423771
good_angle_mean1.1653459764281375
good_angle_median0.19327267307552995
good_angle_min0.03210879995387549
reward_max-8.485343547887169
reward_mean-37.058994892581275
reward_median-36.00659805776611
reward_min-76.76320248441054
survival_time_max3.8333333333333295
survival_time_mean1.3999999999999997
survival_time_min0.4333333333333333
traveled_tiles_max4
traveled_tiles_mean2.142857142857143
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max1.4333333333333294
valid_direction_mean0.5095238095238092
valid_direction_median0.4666666666666666
valid_direction_min0.1333333333333333
161492097MandanaSamieiCopy of #24: sub 687 by MandanaSamiei (ROS-based Lane Following)aido1_LFV_r2-v3step1-simulationsuccessyes3740:02:19(hidden)
other stats
simulation-passed1
161382088yangzm11Copy of #15: sub 1491 by yangzm11 (Tensorflow template)aido1_LFV_r2-v3step2-scoringsuccessyes3740:01:32(hidden)
survival_time_median0.7666666666666666


other stats
episodes
details{"ep000": {"nsteps": 22, "reward": -47.14723071997816, "good_angle": 0.08449586889362225, "survival_time": 0.7333333333333333, "traveled_tiles": 2, "valid_direction": 0.19999999999999996}, "ep001": {"nsteps": 21, "reward": -48.55466799074341, "good_angle": 0.09802365248579732, "survival_time": 0.7, "traveled_tiles": 1, "valid_direction": 0.1333333333333333}, "ep002": {"nsteps": 52, "reward": -19.43764618246888, "good_angle": 0.14938977436364978, "survival_time": 1.7333333333333354, "traveled_tiles": 2, "valid_direction": 0.30000000000000093}, "ep003": {"nsteps": 20, "reward": -49.66100151722785, "good_angle": 0.17509826136784204, "survival_time": 0.6666666666666666, "traveled_tiles": 1, "valid_direction": 0.23333333333333336}, "ep004": {"nsteps": 64, "reward": -15.74610026163282, "good_angle": 0.4204019527937716, "survival_time": 2.1333333333333355, "traveled_tiles": 2, "valid_direction": 0.8000000000000012}, "ep005": {"nsteps": 28, "reward": -35.319236797721324, "good_angle": 0.07442760318114588, "survival_time": 0.9333333333333332, "traveled_tiles": 2, "valid_direction": 0.23333333333333328}, "ep006": {"nsteps": 23, "reward": -43.21969098897408, "good_angle": 0.06512710513029088, "survival_time": 0.7666666666666666, "traveled_tiles": 2, "valid_direction": 0.1333333333333333}}
good_angle_max0.4204019527937716
good_angle_mean0.15242345974515997
good_angle_median0.09802365248579732
good_angle_min0.06512710513029088
reward_max-15.74610026163282
reward_mean-37.01222492267807
reward_median-43.21969098897408
reward_min-49.66100151722785
survival_time_max2.1333333333333355
survival_time_mean1.0952380952380958
survival_time_min0.6666666666666666
traveled_tiles_max2
traveled_tiles_mean1.7142857142857142
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max0.8000000000000012
valid_direction_mean0.2904761904761908
valid_direction_median0.23333333333333328
valid_direction_min0.1333333333333333
161262081DavidCopy of #8: sub 1139 by David (Pytorch IL)aido1_LFV_r2-v3step1-simulationsuccessyes3740:02:12(hidden)
other stats
simulation-passed1
160832065BenjaminMy ROS solutionaido1_LF1_r3-v3step3-videossuccessyes3740:01:00(hidden)
other stats
videos1
160652058DavidPytorch ILaido1_LFV_r1-v3step4-vizsuccessyes3740:04:12(hidden)
driven_lanedir_consec_median0.20670977779260488
deviation-center-line_median0.47836535038998623
in-drivable-lane_median0


other stats
deviation-center-line_max0.6729546539141595
deviation-center-line_mean0.3925957459340188
deviation-center-line_min0
deviation-heading_max2.043926917385951
deviation-heading_mean1.161851509653142
deviation-heading_median1.5204646015866692
deviation-heading_min0
driven_any_max0.309329925221523
driven_any_mean0.20174796067380063
driven_any_median0.21118866834251188
driven_any_min0.09752127813971416
driven_lanedir_consec_max0.3037275796824984
driven_lanedir_consec_mean0.16559217114076727
driven_lanedir_consec_min0
driven_lanedir_max0.3037275796824984
driven_lanedir_mean0.16559217114076727
driven_lanedir_median0.20670977779260488
driven_lanedir_min0
in-drivable-lane_max8.866666666666646
in-drivable-lane_mean1.7733333333333292
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.16460317380207248, "driven_lanedir": 0, "in-drivable-lane": 8.866666666666646, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.21118866834251188, "driven_lanedir": 0.20670977779260488, "in-drivable-lane": 0, "deviation-heading": 1.5204646015866692, "deviation-center-line": 0.47836535038998623, "driven_lanedir_consec": 0.20670977779260488}, "ep002": {"driven_any": 0.09752127813971416, "driven_lanedir": 0.09668616914510614, "in-drivable-lane": 0, "deviation-heading": 0.5458831672728275, "deviation-center-line": 0.26218113770193835, "driven_lanedir_consec": 0.09668616914510614}, "ep003": {"driven_any": 0.22609675786318156, "driven_lanedir": 0.22083732908362697, "in-drivable-lane": 0, "deviation-heading": 1.6989828620202632, "deviation-center-line": 0.5494775876640099, "driven_lanedir_consec": 0.22083732908362697}, "ep004": {"driven_any": 0.309329925221523, "driven_lanedir": 0.3037275796824984, "in-drivable-lane": 0, "deviation-heading": 2.043926917385951, "deviation-center-line": 0.6729546539141595, "driven_lanedir_consec": 0.3037275796824984}}
160111984BenjaminCopy of #6: sub 1063 by Benjamin (My ROS solution)aido1_LFV_r1-v3step4-vizsuccessyes3740:04:53(hidden)
driven_lanedir_consec_median0.5224737162980104
deviation-center-line_median2.2230339114090194
in-drivable-lane_median0


other stats
deviation-center-line_max2.3085565939599557
deviation-center-line_mean1.875148635455823
deviation-center-line_min0.3696382303368952
deviation-heading_max1.2842023423659004
deviation-heading_mean0.934758153794874
deviation-heading_median1.016894632377073
deviation-heading_min0.4662437755018448
driven_any_max0.5401823312833094
driven_any_mean0.4522037871098093
driven_any_median0.5273648773506205
driven_any_min0.13650220441631494
driven_lanedir_consec_max0.5381801984643978
driven_lanedir_consec_mean0.4441950094098054
driven_lanedir_consec_min0.12576139036347483
driven_lanedir_max0.5381801984643978
driven_lanedir_mean0.4441950094098054
driven_lanedir_median0.5224737162980104
driven_lanedir_min0.12576139036347483
in-drivable-lane_max0.6666666666666666
in-drivable-lane_mean0.18666666666666648
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.5273340220570949, "driven_lanedir": 0.5077452629550074, "in-drivable-lane": 0.6666666666666666, "deviation-heading": 0.8636973170714576, "deviation-center-line": 2.199232143992884, "driven_lanedir_consec": 0.5077452629550074}, "ep001": {"driven_any": 0.5296355004417067, "driven_lanedir": 0.526814478968136, "in-drivable-lane": 0, "deviation-heading": 1.016894632377073, "deviation-center-line": 2.27528229758036, "driven_lanedir_consec": 0.526814478968136}, "ep002": {"driven_any": 0.13650220441631494, "driven_lanedir": 0.12576139036347483, "in-drivable-lane": 0.2666666666666657, "deviation-heading": 0.4662437755018448, "deviation-center-line": 0.3696382303368952, "driven_lanedir_consec": 0.12576139036347483}, "ep003": {"driven_any": 0.5273648773506205, "driven_lanedir": 0.5224737162980104, "in-drivable-lane": 0, "deviation-heading": 1.2842023423659004, "deviation-center-line": 2.2230339114090194, "driven_lanedir_consec": 0.5224737162980104}, "ep004": {"driven_any": 0.5401823312833094, "driven_lanedir": 0.5381801984643978, "in-drivable-lane": 0, "deviation-heading": 1.0427527016580955, "deviation-center-line": 2.3085565939599557, "driven_lanedir_consec": 0.5381801984643978}}
160012045WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median2.2666666666666684


other stats
episodes
details{"ep000": {"nsteps": 122, "reward": -7.933433020334752, "good_angle": 5.387241853001284, "survival_time": 4.066666666666662, "traveled_tiles": 9, "valid_direction": 1.2333333333333316}, "ep001": {"nsteps": 500, "reward": 0.3354341311249882, "good_angle": 2.5433902913584268, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 5.033333333333317}, "ep002": {"nsteps": 68, "reward": -15.255837763717864, "good_angle": 3.2909814925368095, "survival_time": 2.2666666666666684, "traveled_tiles": 4, "valid_direction": 1.5666666666666678}, "ep003": {"nsteps": 30, "reward": -33.42212335839868, "good_angle": 1.1261717386148418, "survival_time": 1, "traveled_tiles": 3, "valid_direction": 0.5333333333333332}, "ep004": {"nsteps": 66, "reward": -14.910630772511164, "good_angle": 2.8141644726550648, "survival_time": 2.200000000000002, "traveled_tiles": 5, "valid_direction": 0.5333333333333334}}
good_angle_max5.387241853001284
good_angle_mean3.0323899696332854
good_angle_median2.8141644726550648
good_angle_min1.1261717386148418
reward_max0.3354341311249882
reward_mean-14.237318156767495
reward_median-14.910630772511164
reward_min-33.42212335839868
survival_time_max16.666666666666654
survival_time_mean5.2399999999999975
survival_time_min1
traveled_tiles_max18
traveled_tiles_mean7.8
traveled_tiles_median5
traveled_tiles_min3
valid_direction_max5.033333333333317
valid_direction_mean1.7799999999999965
valid_direction_median1.2333333333333316
valid_direction_min0.5333333333333332
159931985lavoiemsCopy of #7: sub 819 by lavoiems (Baby Duke)aido1_LFV_r1-v3step4-vizsuccessyes3740:01:09(hidden)
driven_lanedir_consec_median0.03122549291936937
deviation-center-line_median0.0924038867646522
in-drivable-lane_median0


other stats
deviation-center-line_max0.27796607344413843
deviation-center-line_mean0.12358480313739516
deviation-center-line_min0.030624379089242367
deviation-heading_max0.6976925341963716
deviation-heading_mean0.37533209940181766
deviation-heading_median0.24689423501550203
deviation-heading_min0.13867855440170726
driven_any_max0.09889047824167214
driven_any_mean0.04139386418811277
driven_any_median0.03523921738026135
driven_any_min0.007886902558323436
driven_lanedir_consec_max0.09637936578948868
driven_lanedir_consec_mean0.03751556629800045
driven_lanedir_consec_min0.0037267722180224
driven_lanedir_max0.09637936578948868
driven_lanedir_mean0.03802964723023933
driven_lanedir_median0.0337958975805638
driven_lanedir_min0.0037267722180224
in-drivable-lane_max0.13333333333333333
in-drivable-lane_mean0.04666666666666673
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.007886902558323436, "driven_lanedir": 0.0037267722180224, "in-drivable-lane": 0.13333333333333333, "deviation-heading": 0.13867855440170726, "deviation-center-line": 0.030624379089242367, "driven_lanedir_consec": 0.0037267722180224}, "ep001": {"driven_any": 0.021725681411106736, "driven_lanedir": 0.021322452837664905, "in-drivable-lane": 0, "deviation-heading": 0.1446905668732553, "deviation-center-line": 0.07418801956746274, "driven_lanedir_consec": 0.021322452837664905}, "ep002": {"driven_any": 0.09889047824167214, "driven_lanedir": 0.09637936578948868, "in-drivable-lane": 0, "deviation-heading": 0.6976925341963716, "deviation-center-line": 0.27796607344413843, "driven_lanedir_consec": 0.09637936578948868}, "ep003": {"driven_any": 0.03523921738026135, "driven_lanedir": 0.03492374772545686, "in-drivable-lane": 0, "deviation-heading": 0.24689423501550203, "deviation-center-line": 0.14274165682148016, "driven_lanedir_consec": 0.03492374772545686}, "ep004": {"driven_any": 0.04322704134920018, "driven_lanedir": 0.0337958975805638, "in-drivable-lane": 0.10000000000000032, "deviation-heading": 0.6487046065222521, "deviation-center-line": 0.0924038867646522, "driven_lanedir_consec": 0.03122549291936937}}
159752042WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step3-videossuccessyes3740:01:54(hidden)
other stats
videos1
159642041BenjaminMy ROS solutionaido1_LF1_r3-v3step4-vizsuccessyes3740:01:05(hidden)
driven_lanedir_median0.8688955414525849
deviation-center-line_median0.05642359309717534
in-drivable-lane_median0.36666666666666664


other stats
deviation-center-line_max0.24063543016284136
deviation-center-line_mean0.09003338587699651
deviation-center-line_min0.02099388952638636
deviation-heading_max0.6542647559924871
deviation-heading_mean0.3164977930649224
deviation-heading_median0.290237413389262
deviation-heading_min0.07655546397152013
driven_any_max3.738614871838621
driven_any_mean1.5572019530321703
driven_any_median1.1493300298490323
driven_any_min0.3732452205781124
driven_lanedir_max2.8517451955233284
driven_lanedir_mean1.0655164930360934
driven_lanedir_min0.10731739634716277
in-drivable-lane_max0.7999999999999999
in-drivable-lane_mean0.3666666666666664
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.1372422491496337, "driven_lanedir": 0.10731739634716277, "in-drivable-lane": 0.7999999999999999, "deviation-heading": 0.290237413389262, "deviation-center-line": 0.0258668957289799}, "ep001": {"driven_any": 1.3875773937454527, "driven_lanedir": 0.8688955414525849, "in-drivable-lane": 0.36666666666666664, "deviation-heading": 0.4309975607887923, "deviation-center-line": 0.1062471208695996}, "ep002": {"driven_any": 0.3732452205781124, "driven_lanedir": 0.3639404665963011, "in-drivable-lane": 0, "deviation-heading": 0.07655546397152013, "deviation-center-line": 0.02099388952638636}, "ep003": {"driven_any": 3.738614871838621, "driven_lanedir": 2.8517451955233284, "in-drivable-lane": 0.6666666666666654, "deviation-heading": 0.6542647559924871, "deviation-center-line": 0.24063543016284136}, "ep004": {"driven_any": 1.1493300298490323, "driven_lanedir": 1.1356838652610906, "in-drivable-lane": 0, "deviation-heading": 0.13043377118255034, "deviation-center-line": 0.05642359309717534}}
159582041BenjaminMy ROS solutionaido1_LF1_r3-v3step3-videossuccessyes3740:00:40(hidden)
other stats
videos1
159422040BenjaminMy ROS solutionaido1_LF1_r3-v3step1-simulationsuccessyes3740:02:23(hidden)
other stats
simulation-passed1
159382033wenhuiCopy of #56: sub 1386 by wenhui (ROS-based Lane Following)aido1_LFV_r1-v3step3-videossuccessyes3740:01:22(hidden)
other stats
videos1
159322033wenhuiCopy of #56: sub 1386 by wenhui (ROS-based Lane Following)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median4.799999999999993


other stats
episodes
details{"ep000": {"nsteps": 373, "reward": -2.3765568739095397, "good_angle": 7.8601607564855085, "survival_time": 12.4333333333333, "traveled_tiles": 8, "valid_direction": 2.16666666666666}, "ep001": {"nsteps": 125, "reward": -7.724582172274589, "good_angle": 5.741737531596992, "survival_time": 4.166666666666662, "traveled_tiles": 4, "valid_direction": 1.03333333333333}, "ep002": {"nsteps": 266, "reward": -3.6778040999508135, "good_angle": 0.4775256871990223, "survival_time": 8.866666666666646, "traveled_tiles": 6, "valid_direction": 1.1666666666666683}, "ep003": {"nsteps": 144, "reward": -6.556782219765915, "good_angle": 3.750253300087512, "survival_time": 4.799999999999993, "traveled_tiles": 5, "valid_direction": 1.1333333333333304}, "ep004": {"nsteps": 102, "reward": -9.483231636677305, "good_angle": 4.67248097420895, "survival_time": 3.3999999999999977, "traveled_tiles": 4, "valid_direction": 1.0999999999999968}}
good_angle_max7.8601607564855085
good_angle_mean4.500431649915598
good_angle_median4.67248097420895
good_angle_min0.4775256871990223
reward_max-2.3765568739095397
reward_mean-5.963791400515632
reward_median-6.556782219765915
reward_min-9.483231636677305
survival_time_max12.4333333333333
survival_time_mean6.73333333333332
survival_time_min3.3999999999999977
traveled_tiles_max8
traveled_tiles_mean5.4
traveled_tiles_median5
traveled_tiles_min4
valid_direction_max2.16666666666666
valid_direction_mean1.3199999999999972
valid_direction_median1.1333333333333304
valid_direction_min1.03333333333333
159172033wenhuiCopy of #56: sub 1386 by wenhui (ROS-based Lane Following)aido1_LFV_r1-v3step1-simulationsuccessyes3740:04:27(hidden)
other stats
simulation-passed1
159062025tomaszfCopy of #48: sub 772 by tomaszf (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step4-vizsuccessyes3740:01:06(hidden)
driven_lanedir_consec_median0.01751575177078521
deviation-center-line_median0.11967685574989692
in-drivable-lane_median0.4666666666666678


other stats
deviation-center-line_max0.13739643113804828
deviation-center-line_mean0.09073843315706934
deviation-center-line_min0
deviation-heading_max0.33523804258471623
deviation-heading_mean0.16250401322100952
deviation-heading_median0.14506764395674576
deviation-heading_min0
driven_any_max0.05866666666666682
driven_any_mean0.039733333333333544
driven_any_median0.042666666666666395
driven_any_min0.017333333333333416
driven_lanedir_consec_max0.04485899203081107
driven_lanedir_consec_mean0.022232027217617113
driven_lanedir_consec_min0
driven_lanedir_max0.04485899203081107
driven_lanedir_mean0.022232027217617113
driven_lanedir_median0.01751575177078521
driven_lanedir_min0
in-drivable-lane_max2.7
in-drivable-lane_mean0.8666666666666665
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.05333333333333429, "driven_lanedir": 0, "in-drivable-lane": 2.7, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.042666666666666395, "driven_lanedir": 0.03148618151834755, "in-drivable-lane": 0.46666666666666656, "deviation-heading": 0.33523804258471623, "deviation-center-line": 0.12313671797317276, "driven_lanedir_consec": 0.03148618151834755}, "ep002": {"driven_any": 0.02666666666666679, "driven_lanedir": 0.01751575177078521, "in-drivable-lane": 0.4666666666666678, "deviation-heading": 0.14506764395674576, "deviation-center-line": 0.0734821609242287, "driven_lanedir_consec": 0.01751575177078521}, "ep003": {"driven_any": 0.05866666666666682, "driven_lanedir": 0.04485899203081107, "in-drivable-lane": 0.6999999999999975, "deviation-heading": 0.27573168571715656, "deviation-center-line": 0.13739643113804828, "driven_lanedir_consec": 0.04485899203081107}, "ep004": {"driven_any": 0.017333333333333416, "driven_lanedir": 0.017299210768141733, "in-drivable-lane": 0, "deviation-heading": 0.056482693846429045, "deviation-center-line": 0.11967685574989692, "driven_lanedir_consec": 0.017299210768141733}}
159022025tomaszfCopy of #48: sub 772 by tomaszf (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step3-videossuccessyes3740:00:40(hidden)
other stats
videos1
158972025tomaszfCopy of #48: sub 772 by tomaszf (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:21(hidden)
survival_time_median2.200000000000002


other stats
episodes
details{"ep000": {"nsteps": 82, "reward": -14.69995212336866, "good_angle": 6.65667704223881, "survival_time": 2.7333333333333334, "traveled_tiles": 3, "valid_direction": 0.9666666666666646}, "ep001": {"nsteps": 66, "reward": -15.58014577536872, "good_angle": 4.307042875025382, "survival_time": 2.200000000000002, "traveled_tiles": 3, "valid_direction": 0.9333333333333348}, "ep002": {"nsteps": 42, "reward": -23.687238975206306, "good_angle": 0.2712250888110818, "survival_time": 1.400000000000001, "traveled_tiles": 2, "valid_direction": 0.5666666666666678}, "ep003": {"nsteps": 90, "reward": -11.363539161284764, "good_angle": 5.194875623318005, "survival_time": 2.999999999999999, "traveled_tiles": 3, "valid_direction": 0.93333333333333}, "ep004": {"nsteps": 28, "reward": -35.45923779612141, "good_angle": 0.003544771893499332, "survival_time": 0.9333333333333332, "traveled_tiles": 2, "valid_direction": 0}}
good_angle_max6.65667704223881
good_angle_mean3.286673080257356
good_angle_median4.307042875025382
good_angle_min0.003544771893499332
reward_max-11.363539161284764
reward_mean-20.158022766269973
reward_median-15.58014577536872
reward_min-35.45923779612141
survival_time_max2.999999999999999
survival_time_mean2.0533333333333337
survival_time_min0.9333333333333332
traveled_tiles_max3
traveled_tiles_mean2.6
traveled_tiles_median3
traveled_tiles_min2
valid_direction_max0.9666666666666646
valid_direction_mean0.6799999999999994
valid_direction_median0.93333333333333
valid_direction_min0
158882025tomaszfCopy of #48: sub 772 by tomaszf (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step1-simulationsuccessyes3740:01:20(hidden)
other stats
simulation-passed1
158782019kbensonCopy of #42: sub 769 by kbenson (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step4-vizsuccessyes3740:00:59(hidden)
driven_lanedir_consec_median0.016896139392853848
deviation-center-line_median0.05762218980550593
in-drivable-lane_median0.2333333333333335


other stats
deviation-center-line_max0.06954959533320816
deviation-center-line_mean0.04432844166222049
deviation-center-line_min0
deviation-heading_max0.1586328052415729
deviation-heading_mean0.07899343004344375
deviation-heading_median0.06767985204453465
deviation-heading_min0
driven_any_max0.05733333333333349
driven_any_mean0.03840000000000016
driven_any_median0.04133333333333323
driven_any_min0.01599999999999999
driven_lanedir_consec_max0.044811537971223385
driven_lanedir_consec_mean0.021737522654936052
driven_lanedir_consec_min0
driven_lanedir_max0.044811537971223385
driven_lanedir_mean0.021737522654936052
driven_lanedir_median0.016896139392853848
driven_lanedir_min0
in-drivable-lane_max1.333333333333334
in-drivable-lane_mean0.4266666666666671
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.05200000000000069, "driven_lanedir": 0, "in-drivable-lane": 1.333333333333334, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.04133333333333323, "driven_lanedir": 0.0310114344627058, "in-drivable-lane": 0.2333333333333335, "deviation-heading": 0.1586328052415729, "deviation-center-line": 0.059749251159330614, "driven_lanedir_consec": 0.0310114344627058}, "ep002": {"driven_any": 0.0253333333333334, "driven_lanedir": 0.016896139392853848, "in-drivable-lane": 0.23333333333333336, "deviation-heading": 0.06767985204453465, "deviation-center-line": 0.03472117201305775, "driven_lanedir_consec": 0.016896139392853848}, "ep003": {"driven_any": 0.05733333333333349, "driven_lanedir": 0.044811537971223385, "in-drivable-lane": 0.33333333333333437, "deviation-heading": 0.14145912181986756, "deviation-center-line": 0.06954959533320816, "driven_lanedir_consec": 0.044811537971223385}, "ep004": {"driven_any": 0.01599999999999999, "driven_lanedir": 0.015968501447897247, "in-drivable-lane": 0, "deviation-heading": 0.02719537111124361, "deviation-center-line": 0.05762218980550593, "driven_lanedir_consec": 0.015968501447897247}}
158722019kbensonCopy of #42: sub 769 by kbenson (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step3-videossuccessyes3740:00:35(hidden)
other stats
videos1
158652015marshinCopy of #39: sub 756 by marshin (PyTorch template)aido1_LFV_r1-v3step4-vizsuccessyes3740:01:10(hidden)
driven_lanedir_consec_median0.01915979366320792
deviation-center-line_median0.0702872082775904
in-drivable-lane_median0.7333333333333353


other stats
deviation-center-line_max0.11975255398052184
deviation-center-line_mean0.06735031266130494
deviation-center-line_min0
deviation-heading_max0.6481206275034523
deviation-heading_mean0.289508201413683
deviation-heading_median0.2732506034861322
deviation-heading_min0
driven_any_max0.04084430872809388
driven_any_mean0.03193562873185678
driven_any_median0.03171576938661247
driven_any_min0.021125648191151068
driven_lanedir_consec_max0.027801989605771873
driven_lanedir_consec_mean0.018354085262881948
driven_lanedir_consec_min0
driven_lanedir_max0.027801989605771873
driven_lanedir_mean0.018354085262881948
driven_lanedir_median0.01915979366320792
driven_lanedir_min0
in-drivable-lane_max1.1333333333333362
in-drivable-lane_mean0.7466666666666676
in-drivable-lane_min0.03333333333333344
per-episodes
details{"ep000": {"driven_any": 0.021125648191151068, "driven_lanedir": 0, "in-drivable-lane": 1.1, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.035197699208087505, "driven_lanedir": 0.01820520982167788, "in-drivable-lane": 1.1333333333333362, "deviation-heading": 0.21101452425485828, "deviation-center-line": 0.04483162751938835, "driven_lanedir_consec": 0.01820520982167788}, "ep002": {"driven_any": 0.03079471814533894, "driven_lanedir": 0.027801989605771873, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.6481206275034523, "deviation-center-line": 0.11975255398052184, "driven_lanedir_consec": 0.027801989605771873}, "ep003": {"driven_any": 0.04084430872809388, "driven_lanedir": 0.026603433223752054, "in-drivable-lane": 0.7333333333333332, "deviation-heading": 0.3151552518239723, "deviation-center-line": 0.10188017352902412, "driven_lanedir_consec": 0.026603433223752054}, "ep004": {"driven_any": 0.03171576938661247, "driven_lanedir": 0.01915979366320792, "in-drivable-lane": 0.7333333333333353, "deviation-heading": 0.2732506034861322, "deviation-center-line": 0.0702872082775904, "driven_lanedir_consec": 0.01915979366320792}}
158582017morraaCopy of #41: sub 778 by morraa (AMOD18-AIDO not that random execution)aido1_LFV_r1-v3step3-videossuccessyes3740:00:35(hidden)
other stats
videos1
158472010inat777Copy of #34: sub 897 by inat777 (Random execution)aido1_LFV_r1-v3step4-vizsuccessyes3740:01:30(hidden)
driven_lanedir_consec_median0.03127713822938621
deviation-center-line_median0.2287557603071306
in-drivable-lane_median0.6999999999999975


other stats
deviation-center-line_max0.34880876489517604
deviation-center-line_mean0.20025895481285183
deviation-center-line_min0.06910247832492722
deviation-heading_max0.6957154360020401
deviation-heading_mean0.4337381477654508
deviation-heading_median0.5430154724467928
deviation-heading_min0.06838771435531782
driven_any_max0.05894643568179044
driven_any_mean0.04573736221569123
driven_any_median0.044515802866195824
driven_any_min0.026968897489656453
driven_lanedir_consec_max0.046481487855548016
driven_lanedir_consec_mean0.02639750996113732
driven_lanedir_consec_min0.00562527838864362
driven_lanedir_max0.046481487855548016
driven_lanedir_mean0.02639750996113732
driven_lanedir_median0.03127713822938621
driven_lanedir_min0.00562527838864362
in-drivable-lane_max3.566666666666664
in-drivable-lane_mean1.273333333333332
in-drivable-lane_min0.6333333333333311
per-episodes
details{"ep000": {"driven_any": 0.054669547371440266, "driven_lanedir": 0.00562527838864362, "in-drivable-lane": 3.566666666666664, "deviation-heading": 0.06838771435531782, "deviation-center-line": 0.06910247832492722, "driven_lanedir_consec": 0.00562527838864362}, "ep001": {"driven_any": 0.04358612766937315, "driven_lanedir": 0.03127713822938621, "in-drivable-lane": 0.6333333333333311, "deviation-heading": 0.6957154360020401, "deviation-center-line": 0.2287557603071306, "driven_lanedir_consec": 0.03127713822938621}, "ep002": {"driven_any": 0.026968897489656453, "driven_lanedir": 0.015958379307299472, "in-drivable-lane": 0.8000000000000023, "deviation-heading": 0.1894570094090173, "deviation-center-line": 0.10041211223278292, "driven_lanedir_consec": 0.015958379307299472}, "ep003": {"driven_any": 0.05894643568179044, "driven_lanedir": 0.046481487855548016, "in-drivable-lane": 0.6666666666666643, "deviation-heading": 0.6721151066140862, "deviation-center-line": 0.34880876489517604, "driven_lanedir_consec": 0.046481487855548016}, "ep004": {"driven_any": 0.044515802866195824, "driven_lanedir": 0.032645266024809313, "in-drivable-lane": 0.6999999999999975, "deviation-heading": 0.5430154724467928, "deviation-center-line": 0.2542156583042425, "driven_lanedir_consec": 0.032645266024809313}}
158402010inat777Copy of #34: sub 897 by inat777 (Random execution)aido1_LFV_r1-v3step3-videossuccessyes3740:00:41(hidden)
other stats
videos1
158372011haliangCopy of #35: sub 784 by haliang (Random execution)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median3.1999999999999984


other stats
episodes
details{"ep000": {"nsteps": 116, "reward": -11.060436552454686, "good_angle": 8.744156649445554, "survival_time": 3.8666666666666623, "traveled_tiles": 3, "valid_direction": 1.3333333333333286}, "ep001": {"nsteps": 96, "reward": -10.82906149017314, "good_angle": 6.553104946601595, "survival_time": 3.1999999999999984, "traveled_tiles": 3, "valid_direction": 1.3333333333333293}, "ep002": {"nsteps": 61, "reward": -16.360546035203534, "good_angle": 0.4483463386096675, "survival_time": 2.033333333333336, "traveled_tiles": 2, "valid_direction": 0.8333333333333355}, "ep003": {"nsteps": 129, "reward": -7.835350175236547, "good_angle": 6.9736415192198615, "survival_time": 4.2999999999999945, "traveled_tiles": 4, "valid_direction": 1.2999999999999954}, "ep004": {"nsteps": 26, "reward": -38.19977452147465, "good_angle": 0.010812122122522316, "survival_time": 0.8666666666666666, "traveled_tiles": 2, "valid_direction": 0}}
good_angle_max8.744156649445554
good_angle_mean4.546012315199841
good_angle_median6.553104946601595
good_angle_min0.010812122122522316
reward_max-7.835350175236547
reward_mean-16.857033754908514
reward_median-11.060436552454686
reward_min-38.19977452147465
survival_time_max4.2999999999999945
survival_time_mean2.8533333333333317
survival_time_min0.8666666666666666
traveled_tiles_max4
traveled_tiles_mean2.8
traveled_tiles_median3
traveled_tiles_min2
valid_direction_max1.3333333333333293
valid_direction_mean0.959999999999998
valid_direction_median1.2999999999999954
valid_direction_min0
158302008iban_alexCopy of #32: sub 651 by iban_alex (PyTorch template)aido1_LFV_r1-v3step3-videossuccessyes3740:00:40(hidden)
other stats
videos1
158242008iban_alexCopy of #32: sub 651 by iban_alex (PyTorch template)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median1.633333333333335


other stats
episodes
details{"ep000": {"nsteps": 31, "reward": -33.84650739546745, "good_angle": 0.1584885100649551, "survival_time": 1.0333333333333332, "traveled_tiles": 2, "valid_direction": 0.5999999999999999}, "ep001": {"nsteps": 59, "reward": -18.032544525687474, "good_angle": 2.986736673719576, "survival_time": 1.9666666666666697, "traveled_tiles": 3, "valid_direction": 1.7000000000000026}, "ep002": {"nsteps": 49, "reward": -19.754490899758377, "good_angle": 0.2087967000384492, "survival_time": 1.633333333333335, "traveled_tiles": 2, "valid_direction": 0.5000000000000016}, "ep003": {"nsteps": 67, "reward": -14.972104730112338, "good_angle": 0.4715184262615869, "survival_time": 2.233333333333335, "traveled_tiles": 2, "valid_direction": 0.9666666666666678}, "ep004": {"nsteps": 39, "reward": -26.39012629032517, "good_angle": 0.719017326705073, "survival_time": 1.3000000000000007, "traveled_tiles": 2, "valid_direction": 1.133333333333334}}
good_angle_max2.986736673719576
good_angle_mean0.908911527357928
good_angle_median0.4715184262615869
good_angle_min0.1584885100649551
reward_max-14.972104730112338
reward_mean-22.59915476827016
reward_median-19.754490899758377
reward_min-33.84650739546745
survival_time_max2.233333333333335
survival_time_mean1.633333333333335
survival_time_min1.0333333333333332
traveled_tiles_max3
traveled_tiles_mean2.2
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max1.7000000000000026
valid_direction_mean0.9800000000000012
valid_direction_median0.9666666666666678
valid_direction_min0.5000000000000016
158092002krishnaCopy of #25: sub 1143 by krishna (gym_duckietown + opencv)aido1_LFV_r1-v3step4-vizsuccessyes3740:02:13(hidden)
driven_lanedir_consec_median0.017764974465042827
deviation-center-line_median0.3967637303437058
in-drivable-lane_median1.566666666666661


other stats
deviation-center-line_max0.46230332202814456
deviation-center-line_mean0.30331626139841583
deviation-center-line_min0
deviation-heading_max1.151154841641802
deviation-heading_mean0.5460170707131593
deviation-heading_median0.4643402860238082
deviation-heading_min0
driven_any_max0.05939999999999951
driven_any_mean0.04037333333333374
driven_any_median0.04359999999999837
driven_any_min0.017799999999999757
driven_lanedir_consec_max0.04511880255334362
driven_lanedir_consec_mean0.022443907746332113
driven_lanedir_consec_min0
driven_lanedir_max0.04511880255334362
driven_lanedir_mean0.022443907746332113
driven_lanedir_median0.017764974465042827
driven_lanedir_min0
in-drivable-lane_max9.099999999999978
in-drivable-lane_mean2.9133333333333256
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.05386666666667084, "driven_lanedir": 0, "in-drivable-lane": 9.099999999999978, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.04359999999999837, "driven_lanedir": 0.03178843952847462, "in-drivable-lane": 1.566666666666661, "deviation-heading": 1.151154841641802, "deviation-center-line": 0.41812650752259223, "driven_lanedir_consec": 0.03178843952847462}, "ep002": {"driven_any": 0.02720000000000022, "driven_lanedir": 0.01754732218479949, "in-drivable-lane": 1.566666666666661, "deviation-heading": 0.4643402860238082, "deviation-center-line": 0.2393877470976365, "driven_lanedir_consec": 0.01754732218479949}, "ep003": {"driven_any": 0.05939999999999951, "driven_lanedir": 0.04511880255334362, "in-drivable-lane": 2.333333333333326, "deviation-heading": 0.9263145797454232, "deviation-center-line": 0.46230332202814456, "driven_lanedir_consec": 0.04511880255334362}, "ep004": {"driven_any": 0.017799999999999757, "driven_lanedir": 0.017764974465042827, "in-drivable-lane": 0, "deviation-heading": 0.18827564615476303, "deviation-center-line": 0.3967637303437058, "driven_lanedir_consec": 0.017764974465042827}}
158062003jahanviCopy of #27: sub 422 by jahanvi (Random execution)aido1_LFV_r1-v3step3-videossuccessyes3740:00:39(hidden)
other stats
videos1
158022003jahanviCopy of #27: sub 422 by jahanvi (Random execution)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:22(hidden)
survival_time_median2.0000000000000027


other stats
episodes
details{"ep000": {"nsteps": 57, "reward": -19.122842364143903, "good_angle": 0.08711919989969064, "survival_time": 1.9000000000000024, "traveled_tiles": 2, "valid_direction": 0}, "ep001": {"nsteps": 95, "reward": -10.935368987761043, "good_angle": 5.773766783810334, "survival_time": 3.166666666666665, "traveled_tiles": 3, "valid_direction": 1.3333333333333297}, "ep002": {"nsteps": 60, "reward": -16.32889733593911, "good_angle": 0.35227508309499583, "survival_time": 2.0000000000000027, "traveled_tiles": 2, "valid_direction": 0.7333333333333354}, "ep003": {"nsteps": 128, "reward": -8.252317506354302, "good_angle": 7.742359033639803, "survival_time": 4.266666666666661, "traveled_tiles": 4, "valid_direction": 1.2999999999999954}, "ep004": {"nsteps": 34, "reward": -29.1441361958928, "good_angle": 0.008112047323548961, "survival_time": 1.1333333333333335, "traveled_tiles": 2, "valid_direction": 0}}
good_angle_max7.742359033639803
good_angle_mean2.7927264295536744
good_angle_median0.35227508309499583
good_angle_min0.008112047323548961
reward_max-8.252317506354302
reward_mean-16.756712478018233
reward_median-16.32889733593911
reward_min-29.1441361958928
survival_time_max4.266666666666661
survival_time_mean2.493333333333333
survival_time_min1.1333333333333335
traveled_tiles_max4
traveled_tiles_mean2.6
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max1.3333333333333297
valid_direction_mean0.673333333333332
valid_direction_median0.7333333333333354
valid_direction_min0
157931998pravishsainathCopy of #21: sub 1085 by pravishsainath (PyTorch DDPG template)aido1_LFV_r1-v3step3-videossuccessyes3740:01:33(hidden)
other stats
videos1
157892000tnd_duck18Copy of #23: sub 1225 by tnd_duck18 (ROS-based Lane Following)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median3.166666666666665


other stats
episodes
details{"ep000": {"nsteps": 118, "reward": -8.533995352239522, "good_angle": 1.312761118752576, "survival_time": 3.933333333333329, "traveled_tiles": 3, "valid_direction": 1.066666666666664}, "ep001": {"nsteps": 95, "reward": -10.117814361735396, "good_angle": 2.8791666676758916, "survival_time": 3.166666666666665, "traveled_tiles": 4, "valid_direction": 0.999999999999997}, "ep002": {"nsteps": 16, "reward": -62.267705857753754, "good_angle": 0.33162523374720204, "survival_time": 0.5333333333333333, "traveled_tiles": 1, "valid_direction": 0.3}, "ep003": {"nsteps": 388, "reward": -2.1256155350927224, "good_angle": 7.376966383131208, "survival_time": 12.933333333333298, "traveled_tiles": 9, "valid_direction": 2.2333333333333263}, "ep004": {"nsteps": 30, "reward": -33.07557328790426, "good_angle": 0.09751719256212904, "survival_time": 1, "traveled_tiles": 2, "valid_direction": 0.1333333333333333}}
good_angle_max7.376966383131208
good_angle_mean2.399607319173801
good_angle_median1.312761118752576
good_angle_min0.09751719256212904
reward_max-2.1256155350927224
reward_mean-23.22414087894513
reward_median-10.117814361735396
reward_min-62.267705857753754
survival_time_max12.933333333333298
survival_time_mean4.3133333333333255
survival_time_min0.5333333333333333
traveled_tiles_max9
traveled_tiles_mean3.8
traveled_tiles_median3
traveled_tiles_min1
valid_direction_max2.2333333333333263
valid_direction_mean0.946666666666664
valid_direction_median0.999999999999997
valid_direction_min0.1333333333333333
157852000tnd_duck18Copy of #23: sub 1225 by tnd_duck18 (ROS-based Lane Following)aido1_LFV_r1-v3step1-simulationsuccessyes3740:03:27(hidden)
other stats
simulation-passed1
157751994trimcaoCopy of #17: sub 1515 by trimcao (Tensorflow template)aido1_LFV_r1-v3step4-vizsuccessyes3740:01:15(hidden)
driven_lanedir_consec_median0.023290153112226444
deviation-center-line_median0.13135875163998006
in-drivable-lane_median0.03333333333333344


other stats
deviation-center-line_max0.4102592419992988
deviation-center-line_mean0.18630073398571917
deviation-center-line_min0.07201676359142307
deviation-heading_max0.6864165307733071
deviation-heading_mean0.3707926797998172
deviation-heading_median0.336115272219095
deviation-heading_min0.1810792893668544
driven_any_max0.10387078068695162
driven_any_mean0.04440333369988021
driven_any_median0.03307523801881645
driven_any_min0.02254145773500417
driven_lanedir_consec_max0.10262643803374048
driven_lanedir_consec_mean0.041248975828050774
driven_lanedir_consec_min0.0203568997214555
driven_lanedir_max0.10262643803374048
driven_lanedir_mean0.041248975828050774
driven_lanedir_median0.023290153112226444
driven_lanedir_min0.0203568997214555
in-drivable-lane_max0.7999999999999999
in-drivable-lane_mean0.17333333333333337
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.03307523801881645, "driven_lanedir": 0.0203568997214555, "in-drivable-lane": 0.7999999999999999, "deviation-heading": 0.386916800913653, "deviation-center-line": 0.07201676359142307, "driven_lanedir_consec": 0.0203568997214555}, "ep001": {"driven_any": 0.02254145773500417, "driven_lanedir": 0.02228517319393545, "in-drivable-lane": 0, "deviation-heading": 0.1810792893668544, "deviation-center-line": 0.12189535491283816, "driven_lanedir_consec": 0.02228517319393545}, "ep002": {"driven_any": 0.10387078068695162, "driven_lanedir": 0.10262643803374048, "in-drivable-lane": 0, "deviation-heading": 0.6864165307733071, "deviation-center-line": 0.4102592419992988, "driven_lanedir_consec": 0.10262643803374048}, "ep003": {"driven_any": 0.03840942730664932, "driven_lanedir": 0.03768621507889603, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.2634355057261767, "deviation-center-line": 0.19597355778505576, "driven_lanedir_consec": 0.03768621507889603}, "ep004": {"driven_any": 0.024119764751979533, "driven_lanedir": 0.023290153112226444, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.336115272219095, "deviation-center-line": 0.13135875163998006, "driven_lanedir_consec": 0.023290153112226444}}
157681994trimcaoCopy of #17: sub 1515 by trimcao (Tensorflow template)aido1_LFV_r1-v3step3-videossuccessyes3740:00:42(hidden)
other stats
videos1
157641994trimcaoCopy of #17: sub 1515 by trimcao (Tensorflow template)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median1.8666666666666691


other stats
episodes
details{"ep000": {"nsteps": 56, "reward": -17.87443057796918, "good_angle": 0.33861011572124705, "survival_time": 1.8666666666666691, "traveled_tiles": 2, "valid_direction": 0.7333333333333344}, "ep001": {"nsteps": 40, "reward": -24.525547401385847, "good_angle": 0.04263467527254096, "survival_time": 1.333333333333334, "traveled_tiles": 2, "valid_direction": 0.10000000000000032}, "ep002": {"nsteps": 158, "reward": -5.820570118395211, "good_angle": 0.16586691376844898, "survival_time": 5.266666666666658, "traveled_tiles": 6, "valid_direction": 0.23333333333333328}, "ep003": {"nsteps": 58, "reward": -16.853606580403344, "good_angle": 0.1400347453644999, "survival_time": 1.933333333333336, "traveled_tiles": 2, "valid_direction": 0.2333333333333341}, "ep004": {"nsteps": 41, "reward": -24.03005574534579, "good_angle": 0.2185612956903061, "survival_time": 1.3666666666666676, "traveled_tiles": 2, "valid_direction": 0.3666666666666676}}
good_angle_max0.33861011572124705
good_angle_mean0.18114154916340855
good_angle_median0.16586691376844898
good_angle_min0.04263467527254096
reward_max-5.820570118395211
reward_mean-17.820842084699873
reward_median-17.87443057796918
reward_min-24.525547401385847
survival_time_max5.266666666666658
survival_time_mean2.3533333333333326
survival_time_min1.333333333333334
traveled_tiles_max6
traveled_tiles_mean2.8
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0.7333333333333344
valid_direction_mean0.3333333333333339
valid_direction_median0.2333333333333341
valid_direction_min0.10000000000000032
157591991tongqiusuiCopy of #13: sub 1363 by tongqiusui (Tensorflow template)aido1_LFV_r1-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median1.633333333333335


other stats
episodes
details{"ep000": {"nsteps": 49, "reward": -21.857054626455113, "good_angle": 0.7528802202459067, "survival_time": 1.633333333333335, "traveled_tiles": 2, "valid_direction": 0.5333333333333347}, "ep001": {"nsteps": 42, "reward": -23.34137199058508, "good_angle": 0.29271881046695, "survival_time": 1.400000000000001, "traveled_tiles": 3, "valid_direction": 0.13333333333333364}, "ep002": {"nsteps": 143, "reward": -6.431394961747256, "good_angle": 0.18456435567274385, "survival_time": 4.7666666666666595, "traveled_tiles": 6, "valid_direction": 0.2666666666666663}, "ep003": {"nsteps": 65, "reward": -15.016069730371235, "good_angle": 0.19390007545778176, "survival_time": 2.1666666666666687, "traveled_tiles": 2, "valid_direction": 0.26666666666666616}, "ep004": {"nsteps": 37, "reward": -26.63846759529583, "good_angle": 0.15976924971662915, "survival_time": 1.2333333333333338, "traveled_tiles": 2, "valid_direction": 0.2666666666666673}}
good_angle_max0.7528802202459067
good_angle_mean0.3167665423120023
good_angle_median0.19390007545778176
good_angle_min0.15976924971662915
reward_max-6.431394961747256
reward_mean-18.656871780890903
reward_median-21.857054626455113
reward_min-26.63846759529583
survival_time_max4.7666666666666595
survival_time_mean2.24
survival_time_min1.2333333333333338
traveled_tiles_max6
traveled_tiles_mean3
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0.5333333333333347
valid_direction_mean0.29333333333333356
valid_direction_median0.2666666666666663
valid_direction_min0.13333333333333364
157511994trimcaoCopy of #17: sub 1515 by trimcao (Tensorflow template)aido1_LFV_r1-v3step1-simulationsuccessyes3740:01:22(hidden)
other stats
simulation-passed1
157301975dcharrezPyTorch Sagemaker templateaido1_LF1_r3-v3step3-videossuccessyes3740:01:36(hidden)
other stats
videos1
157111975dcharrezPyTorch Sagemaker templateaido1_LF1_r3-v3step1-simulationsuccessyes3740:04:11(hidden)
other stats
simulation-passed1
157031967Eric LuCopy of #53: sub 1311 by Eric Lu (Baseline solution using imitation learning from logs)aido1_LFV_r1-v3step1-simulationerroryes3740:00:10
Error while running [...]
Error while running Docker Compose:

Could not run ['docker-compose', '-p', 'job15703-7761', 'pull']:

   >  Command '['docker-compose', '-p', 'job15703-7761', 'pull']' returned non-zero exit status 1

stdout | 

stderr | Pulling evaluator ...
stderr | Pulling solution  ...
stderr | 
Pulling solution  ... error

Pulling evaluator ... done

stderr | ERROR: for solution  invalid reference format: repository name must be lowercase
stderr | invalid reference format: repository name must be lowercase
stderr | 
(hidden)
156821941jahanviCopy of #27: sub 422 by jahanvi (Random execution)aido1_LFV_r1-v3step1-simulationerroryes3740:00:11
Error while running [...]
Error while running Docker Compose:

Could not run ['docker-compose', '-p', 'job15682-6513', 'pull']:

   >  Command '['docker-compose', '-p', 'job15682-6513', 'pull']' returned non-zero exit status 1

stdout | 

stderr | Pulling evaluator ...
stderr | Pulling solution  ...
stderr | 
Pulling solution  ... error

Pulling evaluator ... done

stderr | ERROR: for solution  invalid reference format: repository name must be lowercase
stderr | invalid reference format: repository name must be lowercase
stderr | 
(hidden)
156471909Andrea CensiCopy of #3: sub 1859 by miksaz (JetBrains Research)aido1_LF1_r3-v3step3-videossuccessyes3740:02:18(hidden)
other stats
videos1
156401911Andrea CensiCopy of #9: sub 436 by orlando.m.bol@gmail.com (PyTorch DDPG template)aido1_LFV_r1-v3step1-simulationerroryes3740:00:09
Error while running [...]
Error while running Docker Compose:

Could not run ['docker-compose', '-p', 'job15640-2896', 'pull']:

   >  Command '['docker-compose', '-p', 'job15640-2896', 'pull']' returned non-zero exit status 1

stdout | 

stderr | Pulling evaluator ...
stderr | Pulling solution  ...
stderr | 
Pulling solution  ... error

Pulling evaluator ... done

stderr | ERROR: for solution  invalid reference format: repository name must be lowercase
stderr | invalid reference format: repository name must be lowercase
stderr | 
(hidden)
156351896Andrea CensiCopy of #1: sub 1633 by WEIGAO (First trial)aido1_LF1_r3-v3step4-vizsuccessyes3740:04:58(hidden)
driven_lanedir_median18.156089559848223
deviation-center-line_median0.7333855090365675
in-drivable-lane_median0.1999999999999994


other stats
deviation-center-line_max0.7381884846980663
deviation-center-line_mean0.6781565484229586
deviation-center-line_min0.5903781953835372
deviation-heading_max1.8084720569253956
deviation-heading_mean1.669105196855286
deviation-heading_median1.7076031998387446
deviation-heading_min1.4952343696105217
driven_any_max18.790716741842648
driven_any_mean18.614537084394385
driven_any_median18.56548227272789
driven_any_min18.51576164158293
driven_lanedir_max18.36049637322175
driven_lanedir_mean18.150411404678067
driven_lanedir_min17.821007374041756
in-drivable-lane_max0.43333333333333474
in-drivable-lane_mean0.22000000000000028
in-drivable-lane_min0.09999999999999976
per-episodes
details{"ep000": {"driven_any": 18.521021669223412, "driven_lanedir": 17.821007374041756, "in-drivable-lane": 0.43333333333333474, "deviation-heading": 1.7232686872115384, "deviation-center-line": 0.5903781953835372}, "ep001": {"driven_any": 18.51576164158293, "driven_lanedir": 18.078343915735783, "in-drivable-lane": 0.1999999999999994, "deviation-heading": 1.7076031998387446, "deviation-center-line": 0.5931254970134946}, "ep002": {"driven_any": 18.790716741842648, "driven_lanedir": 18.336119800542814, "in-drivable-lane": 0.23333333333333273, "deviation-heading": 1.4952343696105217, "deviation-center-line": 0.7333855090365675}, "ep003": {"driven_any": 18.56548227272789, "driven_lanedir": 18.156089559848223, "in-drivable-lane": 0.13333333333333464, "deviation-heading": 1.8084720569253956, "deviation-center-line": 0.7357050559831273}, "ep004": {"driven_any": 18.679703096595055, "driven_lanedir": 18.36049637322175, "in-drivable-lane": 0.09999999999999976, "deviation-heading": 1.6109476706902306, "deviation-center-line": 0.7381884846980663}}
156271900Andrea CensiCopy of #4: sub 1684 by Jon (JP pipeline)aido1_LF1_r3-v3step3-videossuccessyes3740:02:09(hidden)
other stats
videos1
156111899Andrea CensiCopy of #3: sub 1859 by miksaz (JetBrains Research)aido1_LF1_r3-v3step1-simulationsuccessyes3740:08:45(hidden)
other stats
simulation-passed1
155761886hvrigazovVisteon perception teamaido1_LF1_r3-v3step4-vizsuccessyes3740:02:55(hidden)
driven_lanedir_median2.2524607036154425
deviation-center-line_median0.4908474873131431
in-drivable-lane_median4.633333333333321


other stats
deviation-center-line_max0.8249484208845358
deviation-center-line_mean0.4858467405030753
deviation-center-line_min0.12663014622236324
deviation-heading_max6.657202410108446
deviation-heading_mean3.533177555838124
deviation-heading_median3.7471102440502015
deviation-heading_min1.2301351365372224
driven_any_max9.318179694132889
driven_any_mean6.018946929442999
driven_any_median6.493199434209217
driven_any_min2.018544846044446
driven_lanedir_max2.3159855475880886
driven_lanedir_mean1.6818492127693188
driven_lanedir_min-0.08384457948258395
in-drivable-lane_max9.433333333333335
in-drivable-lane_mean4.586666666666663
in-drivable-lane_min0.5999999999999985
per-episodes
details{"ep000": {"driven_any": 2.9823147908397334, "driven_lanedir": 2.283178141221682, "in-drivable-lane": 0.5999999999999985, "deviation-heading": 1.2301351365372224, "deviation-center-line": 0.2269119322562909}, "ep001": {"driven_any": 6.493199434209217, "driven_lanedir": 2.2524607036154425, "in-drivable-lane": 4.633333333333321, "deviation-heading": 4.346267745358918, "deviation-center-line": 0.4908474873131431}, "ep002": {"driven_any": 9.318179694132889, "driven_lanedir": 2.3159855475880886, "in-drivable-lane": 6.033333333333329, "deviation-heading": 6.657202410108446, "deviation-center-line": 0.7598957158390435}, "ep003": {"driven_any": 9.282495881988709, "driven_lanedir": 1.6414662509039644, "in-drivable-lane": 9.433333333333335, "deviation-heading": 3.7471102440502015, "deviation-center-line": 0.8249484208845358}, "ep004": {"driven_any": 2.018544846044446, "driven_lanedir": -0.08384457948258395, "in-drivable-lane": 2.2333333333333307, "deviation-heading": 1.6851722431358311, "deviation-center-line": 0.12663014622236324}}
155741886hvrigazovVisteon perception teamaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median11.766666666666636


other stats
episodes
details{"ep000": {"nsteps": 135, "reward": -7.209962923281515, "good_angle": 1.099381876606283, "survival_time": 4.499999999999994, "traveled_tiles": 4, "valid_direction": 1.3666666666666645}, "ep001": {"nsteps": 353, "reward": -3.2023205381136113, "good_angle": 8.916819572765696, "survival_time": 11.766666666666636, "traveled_tiles": 5, "valid_direction": 7.866666666666645}, "ep002": {"nsteps": 500, "reward": -0.1707879383006948, "good_angle": 13.385114485139985, "survival_time": 16.666666666666654, "traveled_tiles": 4, "valid_direction": 11.933333333333325}, "ep003": {"nsteps": 500, "reward": -0.8146171500226482, "good_angle": 38.07835363190405, "survival_time": 16.666666666666654, "traveled_tiles": 3, "valid_direction": 12.533333333333328}, "ep004": {"nsteps": 129, "reward": -8.554485860230155, "good_angle": 3.8240446469995057, "survival_time": 4.2999999999999945, "traveled_tiles": 2, "valid_direction": 3.433333333333328}}
good_angle_max38.07835363190405
good_angle_mean13.060742842683103
good_angle_median8.916819572765696
good_angle_min1.099381876606283
reward_max-0.1707879383006948
reward_mean-3.9904348819897257
reward_median-3.2023205381136113
reward_min-8.554485860230155
survival_time_max16.666666666666654
survival_time_mean10.779999999999989
survival_time_min4.2999999999999945
traveled_tiles_max5
traveled_tiles_mean3.6
traveled_tiles_median4
traveled_tiles_min2
valid_direction_max12.533333333333328
valid_direction_mean7.426666666666658
valid_direction_median7.866666666666645
valid_direction_min1.3666666666666645
155711885licinaccessible code but it scores better than withoutaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:02:47(hidden)
other stats
passedtrue
155691884hvrigazovVisteon perception teamaido1_LF1_r3-v3step4-vizsuccessyes3740:02:08(hidden)
driven_lanedir_median2.3574005163548053
deviation-center-line_median0.2764749904228753
in-drivable-lane_median0.06666666666666665


other stats
deviation-center-line_max1.040155365645263
deviation-center-line_mean0.481416943576889
deviation-center-line_min0.17889316211953538
deviation-heading_max3.7921507959810574
deviation-heading_mean1.73638182536245
deviation-heading_median0.916092416251412
deviation-heading_min0.7486094899020289
driven_any_max10.863960308189062
driven_any_mean4.861459592027739
driven_any_median2.71132524752038
driven_any_min1.511899906294216
driven_lanedir_max8.603013747030731
driven_lanedir_mean3.994455929374088
driven_lanedir_min1.2076958943692944
in-drivable-lane_max0.866666666666664
in-drivable-lane_mean0.23999999999999944
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 2.71132524752038, "driven_lanedir": 2.3574005163548053, "in-drivable-lane": 0.26666666666666666, "deviation-heading": 0.916092416251412, "deviation-center-line": 0.2764749904228753}, "ep001": {"driven_any": 2.2561519380975152, "driven_lanedir": 2.049084115284857, "in-drivable-lane": 0, "deviation-heading": 0.7486094899020289, "deviation-center-line": 0.24781273228691808}, "ep002": {"driven_any": 10.863960308189062, "driven_lanedir": 8.603013747030731, "in-drivable-lane": 0.866666666666664, "deviation-heading": 3.7921507959810574, "deviation-center-line": 1.040155365645263}, "ep003": {"driven_any": 6.963960560037523, "driven_lanedir": 5.755085373830754, "in-drivable-lane": 0.06666666666666665, "deviation-heading": 2.337707239812234, "deviation-center-line": 0.6637484674098532}, "ep004": {"driven_any": 1.511899906294216, "driven_lanedir": 1.2076958943692944, "in-drivable-lane": 0, "deviation-heading": 0.887349184865518, "deviation-center-line": 0.17889316211953538}}
155661884hvrigazovVisteon perception teamaido1_LF1_r3-v3step1-simulationsuccessyes3740:04:51(hidden)
other stats
simulation-passed1
155291882licfishyaido1_amod_efficiency_r1-v3step1-simulationerroryes3740:30:32
Timeout: Waited 180 [...]
Timeout:

Waited 1800.67405391 for container to finish. Giving up. 
(hidden)
155251878Andrea CensiCopy of #4: sub 1684 by Jon (JP pipeline)aido1_LFV_r1-v3step1-simulationerroryes3740:00:11
Error while running [...]
Error while running Docker Compose:

Could not run ['docker-compose', '-p', 'job15525-9876', 'pull']:

   >  Command '['docker-compose', '-p', 'job15525-9876', 'pull']' returned non-zero exit status 1

stdout | 

stderr | Pulling evaluator ...
stderr | Pulling solution  ...
stderr | 
Pulling solution  ... error

Pulling evaluator ... done

stderr | ERROR: for solution  invalid reference format: repository name must be lowercase
stderr | invalid reference format: repository name must be lowercase
stderr | 
(hidden)
155221875Andrea CensiCopy of #1: sub 1633 by WEIGAO (First trial)aido1_LFV_r1-v3step1-simulationtimeoutyes3740:31:00(hidden)
15513555Dzenan LapandicBaseline solution using imitation learning from logsaido1_LFV_r1-v3step4-viztimeoutyes3740:31:01(hidden)
154941807WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step4-vizsuccessyes3740:05:57(hidden)
driven_lanedir_consec_median0.6085073635560427
deviation-center-line_median0.28547212079711753
in-drivable-lane_median0.10000000000000032


other stats
deviation-center-line_max0.361538676952159
deviation-center-line_mean0.2608347011814642
deviation-center-line_min0.03793053714434275
deviation-heading_max1.2192391981996802
deviation-heading_mean0.9623041725904188
deviation-heading_median1.180616634942381
deviation-heading_min0.07467839427332859
driven_any_max0.6200581006723322
driven_any_mean0.49867165177292705
driven_any_median0.6157202321975848
driven_any_min0.02889382259736295
driven_lanedir_consec_max0.6164966226406039
driven_lanedir_consec_mean0.49038695756894624
driven_lanedir_consec_min0.01775609037336471
driven_lanedir_max0.6164966226406039
driven_lanedir_mean0.49038695756894624
driven_lanedir_median0.6085073635560427
driven_lanedir_min0.01775609037336471
in-drivable-lane_max0.4
in-drivable-lane_mean0.1733333333333334
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.6157202321975848, "driven_lanedir": 0.5982264920294609, "in-drivable-lane": 0.4, "deviation-heading": 1.134299109165645, "deviation-center-line": 0.2709324583488521, "driven_lanedir_consec": 0.5982264920294609}, "ep001": {"driven_any": 0.6177917310801195, "driven_lanedir": 0.6109482192452592, "in-drivable-lane": 0.10000000000000032, "deviation-heading": 1.2026875263710592, "deviation-center-line": 0.361538676952159, "driven_lanedir_consec": 0.6109482192452592}, "ep002": {"driven_any": 0.02889382259736295, "driven_lanedir": 0.01775609037336471, "in-drivable-lane": 0.36666666666666664, "deviation-heading": 0.07467839427332859, "deviation-center-line": 0.03793053714434275, "driven_lanedir_consec": 0.01775609037336471}, "ep003": {"driven_any": 0.6200581006723322, "driven_lanedir": 0.6164966226406039, "in-drivable-lane": 0, "deviation-heading": 1.180616634942381, "deviation-center-line": 0.28547212079711753, "driven_lanedir_consec": 0.6164966226406039}, "ep004": {"driven_any": 0.6108943723172358, "driven_lanedir": 0.6085073635560427, "in-drivable-lane": 0, "deviation-heading": 1.2192391981996802, "deviation-center-line": 0.34829971266484944, "driven_lanedir_consec": 0.6085073635560427}}
154931315Andrea CensiRandom executionaido1_LFV_r1-v3step4-vizsuccessyes3740:03:32(hidden)
driven_lanedir_consec_median0.018551892954755903
deviation-center-line_median0.18395738545065257
in-drivable-lane_median0.6666666666666643


other stats
deviation-center-line_max0.19820119656659857
deviation-center-line_mean0.13584915597802258
deviation-center-line_min0
deviation-heading_max0.5316410956983737
deviation-heading_mean0.2415446791279112
deviation-heading_median0.2027594323943872
deviation-heading_min0
driven_any_max0.05939879940189638
driven_any_mean0.03601501302434777
driven_any_median0.0321054768291456
driven_any_min0.018589276292290294
driven_lanedir_consec_max0.03448075314912098
driven_lanedir_consec_mean0.020527577834974983
driven_lanedir_consec_min0
driven_lanedir_max0.03448075314912098
driven_lanedir_mean0.020527577834974983
driven_lanedir_median0.018551892954755903
driven_lanedir_min0
in-drivable-lane_max2.3000000000000016
in-drivable-lane_mean1.0733333333333324
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.0321054768291456, "driven_lanedir": 0, "in-drivable-lane": 2.3000000000000016, "deviation-heading": 0, "deviation-center-line": 0, "driven_lanedir_consec": 0}, "ep001": {"driven_any": 0.04315303821347893, "driven_lanedir": 0.03162443354180774, "in-drivable-lane": 0.6666666666666643, "deviation-heading": 0.5316410956983737, "deviation-center-line": 0.1934431609831806, "driven_lanedir_consec": 0.03162443354180774}, "ep002": {"driven_any": 0.026828474384927664, "driven_lanedir": 0.017980809529190296, "in-drivable-lane": 0.6333333333333353, "deviation-heading": 0.2027594323943872, "deviation-center-line": 0.10364403688968128, "driven_lanedir_consec": 0.017980809529190296}, "ep003": {"driven_any": 0.05939879940189638, "driven_lanedir": 0.03448075314912098, "in-drivable-lane": 1.7666666666666604, "deviation-heading": 0.3947983307844637, "deviation-center-line": 0.19820119656659857, "driven_lanedir_consec": 0.03448075314912098}, "ep004": {"driven_any": 0.018589276292290294, "driven_lanedir": 0.018551892954755903, "in-drivable-lane": 0, "deviation-heading": 0.07852453676233151, "deviation-center-line": 0.18395738545065257, "driven_lanedir_consec": 0.018551892954755903}}
15490336Andrea CensiTensorflow templateaido1_LFV_r1-v3step4-vizsuccessyes3740:03:10(hidden)
driven_lanedir_consec_median0.02963545734179284
deviation-center-line_median0.07042545060806492
in-drivable-lane_median0


other stats
deviation-center-line_max0.2526425325892136
deviation-center-line_mean0.10228484956429593
deviation-center-line_min0.041491028833427285
deviation-heading_max0.26487082139415197
deviation-heading_mean0.1729772998208473
deviation-heading_median0.1314091160494784
deviation-heading_min0.07759445471270537
driven_any_max0.1042105089720778
driven_any_mean0.04399819606924924
driven_any_median0.036665209353585373
driven_any_min0.01549154038229662
driven_lanedir_consec_max0.1038861023301314
driven_lanedir_consec_mean0.04035025315363136
driven_lanedir_consec_min0.015180606093226688
driven_lanedir_max0.1038861023301314
driven_lanedir_mean0.04035025315363136
driven_lanedir_median0.02963545734179284
driven_lanedir_min0.015180606093226688
in-drivable-lane_max0.3000000000000008
in-drivable-lane_mean0.10000000000000016
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.036665209353585373, "driven_lanedir": 0.02963545734179284, "in-drivable-lane": 0.2, "deviation-heading": 0.26483139897041413, "deviation-center-line": 0.05937511833515104, "driven_lanedir_consec": 0.02963545734179284}, "ep001": {"driven_any": 0.01549154038229662, "driven_lanedir": 0.015180606093226688, "in-drivable-lane": 0, "deviation-heading": 0.07759445471270537, "deviation-center-line": 0.041491028833427285, "driven_lanedir_consec": 0.015180606093226688}, "ep002": {"driven_any": 0.1042105089720778, "driven_lanedir": 0.1038861023301314, "in-drivable-lane": 0, "deviation-heading": 0.26487082139415197, "deviation-center-line": 0.2526425325892136, "driven_lanedir_consec": 0.1038861023301314}, "ep003": {"driven_any": 0.02380905661827661, "driven_lanedir": 0.023231211965810704, "in-drivable-lane": 0, "deviation-heading": 0.1314091160494784, "deviation-center-line": 0.08749011745562267, "driven_lanedir_consec": 0.023231211965810704}, "ep004": {"driven_any": 0.039814665020009785, "driven_lanedir": 0.02981788803719515, "in-drivable-lane": 0.3000000000000008, "deviation-heading": 0.12618070797748668, "deviation-center-line": 0.07042545060806492, "driven_lanedir_consec": 0.02981788803719515}}
154851706WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LFV_r1-v3step4-vizsuccessyes3740:02:15(hidden)
driven_lanedir_consec_median0.10426684327523632
deviation-center-line_median0.26527908047718657
in-drivable-lane_median0.033333333333333215


other stats
deviation-center-line_max0.4670633613609429
deviation-center-line_mean0.23840238726914065
deviation-center-line_min0.07100909574780352
deviation-heading_max2.514314549295465
deviation-heading_mean1.231856440423154
deviation-heading_median1.2716190175490478
deviation-heading_min0.15127302211236274
driven_any_max0.227193753663408
driven_any_mean0.1096012435872679
driven_any_median0.1280651048834703
driven_any_min0.013147135356274837
driven_lanedir_consec_max0.17859341455312833
driven_lanedir_consec_mean0.08553150684322822
driven_lanedir_consec_min0.012073593169796178
driven_lanedir_max0.17859341455312833
driven_lanedir_mean0.0883612100272528
driven_lanedir_median0.1054016859161923
driven_lanedir_min0.012073593169796178
in-drivable-lane_max0.23333333333333325
in-drivable-lane_mean0.06666666666666662
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.14323321356609572, "driven_lanedir": 0.11841535919535928, "in-drivable-lane": 0.23333333333333325, "deviation-heading": 1.6695612538716238, "deviation-center-line": 0.26527908047718657, "driven_lanedir_consec": 0.10426684327523632}, "ep001": {"driven_any": 0.036367010467090635, "driven_lanedir": 0.027321997301788005, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.5525143592872706, "deviation-center-line": 0.0947468629130852, "driven_lanedir_consec": 0.027321997301788005}, "ep002": {"driven_any": 0.1280651048834703, "driven_lanedir": 0.1054016859161923, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 1.2716190175490478, "deviation-center-line": 0.29391353584668495, "driven_lanedir_consec": 0.1054016859161923}, "ep003": {"driven_any": 0.227193753663408, "driven_lanedir": 0.17859341455312833, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 2.514314549295465, "deviation-center-line": 0.4670633613609429, "driven_lanedir_consec": 0.17859341455312833}, "ep004": {"driven_any": 0.013147135356274837, "driven_lanedir": 0.012073593169796178, "in-drivable-lane": 0, "deviation-heading": 0.15127302211236274, "deviation-center-line": 0.07100909574780352, "driven_lanedir_consec": 0.012073593169796178}}
154781819WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step4-vizsuccessyes3740:06:23(hidden)
driven_lanedir_consec_median0.6110760695727689
deviation-center-line_median0.2970504818173018
in-drivable-lane_median0


other stats
deviation-center-line_max0.3434605030666663
deviation-center-line_mean0.25020264765653955
deviation-center-line_min0.03924404037944615
deviation-heading_max1.1654522701784662
deviation-heading_mean0.9246037887848104
deviation-heading_median1.1408213305142534
deviation-heading_min0.0709115041135786
driven_any_max0.6198292841154635
driven_any_mean0.4986305656675674
driven_any_median0.6152878745750733
driven_any_min0.027127077599475056
driven_lanedir_consec_max0.6168824683979391
driven_lanedir_consec_mean0.4914634176151758
driven_lanedir_consec_min0.017757957613942135
driven_lanedir_max0.6168824683979391
driven_lanedir_mean0.4914634176151758
driven_lanedir_median0.6110760695727689
driven_lanedir_min0.017757957613942135
in-drivable-lane_max0.4
in-drivable-lane_mean0.14666666666666667
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.6152878745750733, "driven_lanedir": 0.5977044817833179, "in-drivable-lane": 0.4, "deviation-heading": 1.1408213305142534, "deviation-center-line": 0.2564272498578904, "driven_lanedir_consec": 0.5977044817833179}, "ep001": {"driven_any": 0.6170724959235753, "driven_lanedir": 0.613896110707911, "in-drivable-lane": 0, "deviation-heading": 1.160384259865077, "deviation-center-line": 0.3434605030666663, "driven_lanedir_consec": 0.613896110707911}, "ep002": {"driven_any": 0.027127077599475056, "driven_lanedir": 0.017757957613942135, "in-drivable-lane": 0.3333333333333333, "deviation-heading": 0.0709115041135786, "deviation-center-line": 0.03924404037944615, "driven_lanedir_consec": 0.017757957613942135}, "ep003": {"driven_any": 0.6198292841154635, "driven_lanedir": 0.6168824683979391, "in-drivable-lane": 0, "deviation-heading": 1.0854495792526777, "deviation-center-line": 0.2970504818173018, "driven_lanedir_consec": 0.6168824683979391}, "ep004": {"driven_any": 0.6138360961242503, "driven_lanedir": 0.6110760695727689, "in-drivable-lane": 0, "deviation-heading": 1.1654522701784662, "deviation-center-line": 0.31483096316139303, "driven_lanedir_consec": 0.6110760695727689}}
154591869licFreq no rebalanceaido1_amod_efficiency_r1-v3step2-scoringsuccessyes3740:00:20(hidden)
efficiency-33.67257859569076


other stats
fleet_size-1000000000
service_quality-20.28668464892319
154491862licNo rebalanceaido1_amod_efficiency_r1-v3step1-simulationsuccessyes3740:03:33(hidden)
other stats
passedtrue
154431854WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step3-videossuccessyes3740:02:46(hidden)
other stats
videos1
154411855miksazJetBrains Researchaido1_LF1_r3-v3step1-simulationfailedyes3740:10:36
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 477, in wrap_evaluator
    cie.wait_for_solution()
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 270, in wait_for_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Time out: Timeout of 600 while waiting for /challenge-solution-output/output-solution.yaml.
(hidden)
154341852BenjaminMy ROS solutionaido1_LF1_r3-v3step4-vizsuccessyes3740:00:56(hidden)
driven_lanedir_median0.7383564319354049
deviation-center-line_median0.037259370897535976
in-drivable-lane_median0.06666666666666676


other stats
deviation-center-line_max0.09330601232635828
deviation-center-line_mean0.05008008572741355
deviation-center-line_min0.028826297647990832
deviation-heading_max0.35999931632725424
deviation-heading_mean0.2132187649813484
deviation-heading_median0.15416191942359697
deviation-heading_min0.1172316514570269
driven_any_max1.588808243481417
driven_any_mean1.0669038675145952
driven_any_median1.1432359598556736
driven_any_min0.6649369300034762
driven_lanedir_max1.402610842950201
driven_lanedir_mean0.7602175862876337
driven_lanedir_min0.18049461477794637
in-drivable-lane_max0.7666666666666666
in-drivable-lane_mean0.22666666666666663
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.1432359598556736, "driven_lanedir": 0.18049461477794637, "in-drivable-lane": 0.7666666666666666, "deviation-heading": 0.29127705364131495, "deviation-center-line": 0.03679437465034084}, "ep001": {"driven_any": 0.6649369300034762, "driven_lanedir": 0.317657763296082, "in-drivable-lane": 0.3, "deviation-heading": 0.15416191942359697, "deviation-center-line": 0.028826297647990832}, "ep002": {"driven_any": 0.7680698757144611, "driven_lanedir": 0.7383564319354049, "in-drivable-lane": 0, "deviation-heading": 0.1434238840575491, "deviation-center-line": 0.037259370897535976}, "ep003": {"driven_any": 1.1694683285179477, "driven_lanedir": 1.161968278478534, "in-drivable-lane": 0, "deviation-heading": 0.1172316514570269, "deviation-center-line": 0.054214373114841855}, "ep004": {"driven_any": 1.588808243481417, "driven_lanedir": 1.402610842950201, "in-drivable-lane": 0.06666666666666676, "deviation-heading": 0.35999931632725424, "deviation-center-line": 0.09330601232635828}}
154271850WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step4-vizsuccessyes3740:06:21(hidden)
driven_lanedir_median17.969699159439333
deviation-center-line_median0.6962781669303916
in-drivable-lane_median0.1999999999999993


other stats
deviation-center-line_max0.7508985955384387
deviation-center-line_mean0.6802359176020872
deviation-center-line_min0.6088928655158115
deviation-heading_max1.972937771565364
deviation-heading_mean1.740252965059209
deviation-heading_median1.6912953390123873
deviation-heading_min1.5274470444208288
driven_any_max18.605202111109335
driven_any_mean18.451159619253495
driven_any_median18.40794890858163
driven_any_min18.293179363080444
driven_lanedir_max18.25312099889813
driven_lanedir_mean17.972199715394886
driven_lanedir_min17.619697986503144
in-drivable-lane_max0.4999999999999993
in-drivable-lane_mean0.21999999999999947
in-drivable-lane_min0.033333333333333215
per-episodes
details{"ep000": {"driven_any": 18.381859070136414, "driven_lanedir": 17.619697986503144, "in-drivable-lane": 0.4999999999999993, "deviation-heading": 1.6912953390123873, "deviation-center-line": 0.6154888276051272}, "ep001": {"driven_any": 18.293179363080444, "driven_lanedir": 17.969699159439333, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 1.972937771565364, "deviation-center-line": 0.6088928655158115}, "ep002": {"driven_any": 18.605202111109335, "driven_lanedir": 18.118247862098116, "in-drivable-lane": 0.23333333333333264, "deviation-heading": 1.6444017363906815, "deviation-center-line": 0.7296211324206671}, "ep003": {"driven_any": 18.567608643359637, "driven_lanedir": 18.25312099889813, "in-drivable-lane": 0.13333333333333286, "deviation-heading": 1.5274470444208288, "deviation-center-line": 0.6962781669303916}, "ep004": {"driven_any": 18.40794890858163, "driven_lanedir": 17.900232570035694, "in-drivable-lane": 0.1999999999999993, "deviation-heading": 1.865182933906783, "deviation-center-line": 0.7508985955384387}}
154221850WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step1-simulationsuccessyes3740:07:32(hidden)
other stats
simulation-passed1
153971840WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step1-simulationsuccessyes3740:04:28(hidden)
other stats
simulation-passed1
153881841WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.944812976539135, "good_angle": 0.3802292479453211, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.6333333333333323}, "ep001": {"nsteps": 256, "reward": -2.961664725968149, "good_angle": 0.1833261390104795, "survival_time": 8.533333333333314, "traveled_tiles": 16, "valid_direction": 0.33333333333333304}, "ep002": {"nsteps": 13, "reward": -76.51204243875466, "good_angle": 0.10312394388472582, "survival_time": 0.4333333333333333, "traveled_tiles": 2, "valid_direction": 0.2333333333333333}, "ep003": {"nsteps": 500, "reward": 1.009445241212845, "good_angle": 0.27770235213595573, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.49999999999999944}, "ep004": {"nsteps": 500, "reward": 0.9643780252337456, "good_angle": 0.24981938963260872, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.26666666666666594}}
good_angle_max0.3802292479453211
good_angle_mean0.23884021452181817
good_angle_median0.24981938963260872
good_angle_min0.10312394388472582
reward_max1.009445241212845
reward_mean-15.311014184347416
reward_median0.944812976539135
reward_min-76.51204243875466
survival_time_max16.666666666666654
survival_time_mean11.793333333333322
survival_time_min0.4333333333333333
traveled_tiles_max18
traveled_tiles_mean14.4
traveled_tiles_median18
traveled_tiles_min2
valid_direction_max0.6333333333333323
valid_direction_mean0.3933333333333328
valid_direction_median0.33333333333333304
valid_direction_min0.2333333333333333
153861841WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step1-simulationsuccessyes3740:05:42(hidden)
other stats
simulation-passed1
153841838WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step4-vizsuccessno3740:01:31(hidden)
deviation-center-line_median0.08871160995601654
in-drivable-lane_median0.4000000000000013


other stats
deviation-center-line_max0.23823409344643132
deviation-center-line_mean0.10610242002205916
deviation-center-line_min0.038887505829950805
deviation-heading_max0.908263009626664
deviation-heading_mean0.4387630769413942
deviation-heading_median0.25353781368640976
deviation-heading_min0.16399100668890992
driven_any_max6.393071365699328
driven_any_mean3.007352805729925
driven_any_median2.417784233478298
driven_any_min0.6340857650396243
driven_lanedir_max5.25005388699765
driven_lanedir_mean2.236155985190744
driven_lanedir_median1.8727992510154163
driven_lanedir_min0.5714941863648665
in-drivable-lane_max1.933333333333334
in-drivable-lane_mean0.673333333333334
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.512742660257297, "driven_lanedir": 1.088414666476998, "in-drivable-lane": 0.4000000000000013, "deviation-heading": 0.22622092276251807, "deviation-center-line": 0.038887505829950805}, "ep001": {"driven_any": 6.393071365699328, "driven_lanedir": 5.25005388699765, "in-drivable-lane": 0.9666666666666678, "deviation-heading": 0.908263009626664, "deviation-center-line": 0.23823409344643132}, "ep002": {"driven_any": 2.417784233478298, "driven_lanedir": 2.3980179350987902, "in-drivable-lane": 0, "deviation-heading": 0.25353781368640976, "deviation-center-line": 0.08871160995601654}, "ep003": {"driven_any": 4.079080004175076, "driven_lanedir": 1.8727992510154163, "in-drivable-lane": 1.933333333333334, "deviation-heading": 0.6418026319424693, "deviation-center-line": 0.11006347383002572}, "ep004": {"driven_any": 0.6340857650396243, "driven_lanedir": 0.5714941863648665, "in-drivable-lane": 0.06666666666666665, "deviation-heading": 0.16399100668890992, "deviation-center-line": 0.05461541704787138}}
153651835WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.9031861689090728, "good_angle": 0.46850856459124707, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.5999999999999992}, "ep001": {"nsteps": 258, "reward": -2.931997538912435, "good_angle": 0.2080722652483159, "survival_time": 8.59999999999998, "traveled_tiles": 16, "valid_direction": 0.3333333333333326}, "ep002": {"nsteps": 13, "reward": -76.42834445948785, "good_angle": 0.08013053810182, "survival_time": 0.4333333333333333, "traveled_tiles": 2, "valid_direction": 0.2}, "ep003": {"nsteps": 500, "reward": 1.0039326172471046, "good_angle": 0.28554627063946747, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.29999999999999916}, "ep004": {"nsteps": 500, "reward": 1.009659119784832, "good_angle": 0.33706535521366343, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.5666666666666664}}
good_angle_max0.46850856459124707
good_angle_mean0.2758645987589028
good_angle_median0.28554627063946747
good_angle_min0.08013053810182
reward_max1.009659119784832
reward_mean-15.288712818491856
reward_median0.9031861689090728
reward_min-76.42834445948785
survival_time_max16.666666666666654
survival_time_mean11.806666666666654
survival_time_min0.4333333333333333
traveled_tiles_max18
traveled_tiles_mean14.4
traveled_tiles_median18
traveled_tiles_min2
valid_direction_max0.5999999999999992
valid_direction_mean0.39999999999999947
valid_direction_median0.3333333333333326
valid_direction_min0.2
153591832madelhoriusTensorflow templateaido1_LF1_r3-v3step3-videossuccessyes3740:01:11(hidden)
other stats
videos1
153581832madelhoriusTensorflow templateaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median9.266666666666644


other stats
episodes
details{"ep000": {"nsteps": 255, "reward": -3.2289562618076872, "good_angle": 1.1346931930881636, "survival_time": 8.49999999999998, "traveled_tiles": 11, "valid_direction": 1.933333333333329}, "ep001": {"nsteps": 385, "reward": -1.9227435327679303, "good_angle": 0.9429759282318948, "survival_time": 12.833333333333298, "traveled_tiles": 15, "valid_direction": 1.6999999999999953}, "ep002": {"nsteps": 411, "reward": -1.807526985767984, "good_angle": 0.7247250775367927, "survival_time": 13.699999999999962, "traveled_tiles": 14, "valid_direction": 1.166666666666664}, "ep003": {"nsteps": 278, "reward": -2.957609235784776, "good_angle": 0.7534854144834433, "survival_time": 9.266666666666644, "traveled_tiles": 9, "valid_direction": 0.9999999999999972}, "ep004": {"nsteps": 70, "reward": -13.692152492489134, "good_angle": 0.5108653120693448, "survival_time": 2.333333333333335, "traveled_tiles": 3, "valid_direction": 0.4666666666666657}}
good_angle_max1.1346931930881636
good_angle_mean0.8133489850819279
good_angle_median0.7534854144834433
good_angle_min0.5108653120693448
reward_max-1.807526985767984
reward_mean-4.721797701723502
reward_median-2.957609235784776
reward_min-13.692152492489134
survival_time_max13.699999999999962
survival_time_mean9.326666666666643
survival_time_min2.333333333333335
traveled_tiles_max15
traveled_tiles_mean10.4
traveled_tiles_median11
traveled_tiles_min3
valid_direction_max1.933333333333329
valid_direction_mean1.2533333333333303
valid_direction_median1.166666666666664
valid_direction_min0.4666666666666657
153481828BenjaminMy ROS solutionaido1_LF1_r3-v3step3-videossuccessyes3740:00:53(hidden)
other stats
videos1
153261822BenjaminMy ROS solutionaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:19(hidden)
survival_time_median0.8333333333333333


other stats
episodes
details{"ep000": {"nsteps": 101, "reward": -9.553558473544436, "good_angle": 0.6878622706744508, "survival_time": 3.3666666666666645, "traveled_tiles": 6, "valid_direction": 1.4}, "ep001": {"nsteps": 28, "reward": -35.908261712906615, "good_angle": 0.4660331564825207, "survival_time": 0.9333333333333332, "traveled_tiles": 2, "valid_direction": 0.7999999999999998}, "ep002": {"nsteps": 17, "reward": -58.42168461312266, "good_angle": 0.08793228095517146, "survival_time": 0.5666666666666667, "traveled_tiles": 2, "valid_direction": 0.23333333333333336}, "ep003": {"nsteps": 22, "reward": -45.082084779034965, "good_angle": 0.2229039862917091, "survival_time": 0.7333333333333333, "traveled_tiles": 2, "valid_direction": 0.5333333333333332}, "ep004": {"nsteps": 25, "reward": -39.653836598694326, "good_angle": 0.2770210537625589, "survival_time": 0.8333333333333333, "traveled_tiles": 2, "valid_direction": 0.6333333333333333}}
good_angle_max0.6878622706744508
good_angle_mean0.3483505496332822
good_angle_median0.2770210537625589
good_angle_min0.08793228095517146
reward_max-9.553558473544436
reward_mean-37.7238852354606
reward_median-39.653836598694326
reward_min-58.42168461312266
survival_time_max3.3666666666666645
survival_time_mean1.2866666666666662
survival_time_min0.5666666666666667
traveled_tiles_max6
traveled_tiles_mean2.8
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max1.4
valid_direction_mean0.72
valid_direction_median0.6333333333333333
valid_direction_min0.23333333333333336
153241823BenjaminMy ROS solutionaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:20(hidden)
survival_time_median1.8000000000000025


other stats
episodes
details{"ep000": {"nsteps": 24, "reward": -42.29765912230747, "good_angle": 0.45559413706747176, "survival_time": 0.7999999999999999, "traveled_tiles": 2, "valid_direction": 0.49999999999999994}, "ep001": {"nsteps": 25, "reward": -40.72939567089081, "good_angle": 0.4961595004963729, "survival_time": 0.8333333333333333, "traveled_tiles": 2, "valid_direction": 0.5999999999999999}, "ep002": {"nsteps": 54, "reward": -17.780984139966744, "good_angle": 0.03811616478129965, "survival_time": 1.8000000000000025, "traveled_tiles": 4, "valid_direction": 0}, "ep003": {"nsteps": 128, "reward": -7.6670905844976, "good_angle": 0.7041400030387512, "survival_time": 4.266666666666661, "traveled_tiles": 8, "valid_direction": 1.499999999999999}, "ep004": {"nsteps": 195, "reward": -4.779416280083406, "good_angle": 1.4288763989251072, "survival_time": 6.499999999999987, "traveled_tiles": 10, "valid_direction": 3.399999999999992}}
good_angle_max1.4288763989251072
good_angle_mean0.6245772408618006
good_angle_median0.4961595004963729
good_angle_min0.03811616478129965
reward_max-4.779416280083406
reward_mean-22.650909159549208
reward_median-17.780984139966744
reward_min-42.29765912230747
survival_time_max6.499999999999987
survival_time_mean2.8399999999999967
survival_time_min0.7999999999999999
traveled_tiles_max10
traveled_tiles_mean5.2
traveled_tiles_median4
traveled_tiles_min2
valid_direction_max3.399999999999992
valid_direction_mean1.1999999999999982
valid_direction_median0.5999999999999999
valid_direction_min0
153061819WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step1-simulationsuccessyes3740:09:35(hidden)
other stats
simulation-passed1
152941813BenjaminMy ROS solutionaido1_LF1_r3-v3step4-vizsuccessyes3740:01:05(hidden)
driven_lanedir_median0.6464334462107617
deviation-center-line_median0.06120107463650063
in-drivable-lane_median0.4666666666666667


other stats
deviation-center-line_max0.12021391580849772
deviation-center-line_mean0.0636365640309676
deviation-center-line_min0.017058426505454465
deviation-heading_max0.5278659052498437
deviation-heading_mean0.3781772309170023
deviation-heading_median0.42701936627075254
deviation-heading_min0.1271940039532603
driven_any_max2.533297479617228
driven_any_mean1.394093829892555
driven_any_median1.173817032556562
driven_any_min0.7718126262263798
driven_lanedir_max1.453410442542338
driven_lanedir_mean0.6856923653500273
driven_lanedir_min0.027633002421289857
in-drivable-lane_max0.9666666666666668
in-drivable-lane_mean0.4933333333333332
in-drivable-lane_min0.06666666666666665
per-episodes
details{"ep000": {"driven_any": 1.173817032556562, "driven_lanedir": 0.027633002421289857, "in-drivable-lane": 0.9666666666666668, "deviation-heading": 0.1271940039532603, "deviation-center-line": 0.017058426505454465}, "ep001": {"driven_any": 1.380721342530841, "driven_lanedir": 0.6464334462107617, "in-drivable-lane": 0.4666666666666667, "deviation-heading": 0.43626771866514275, "deviation-center-line": 0.06120107463650063}, "ep002": {"driven_any": 0.7718126262263798, "driven_lanedir": 0.5318317162852093, "in-drivable-lane": 0.06666666666666665, "deviation-heading": 0.372539160446012, "deviation-center-line": 0.053541360735889186}, "ep003": {"driven_any": 1.110820668531764, "driven_lanedir": 0.7691532192905373, "in-drivable-lane": 0.1333333333333333, "deviation-heading": 0.42701936627075254, "deviation-center-line": 0.06616804246849595}, "ep004": {"driven_any": 2.533297479617228, "driven_lanedir": 1.453410442542338, "in-drivable-lane": 0.8333333333333325, "deviation-heading": 0.5278659052498437, "deviation-center-line": 0.12021391580849772}}
152801810WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step3-videossuccessyes3740:02:50(hidden)
other stats
videos1
152691807WEIGAOPanasonic R&D Center Singapore & NUSaido1_LFV_r1-v3step3-videossuccessyes3740:02:11(hidden)
other stats
videos1
152591804hvrigazovVisteon perception teamaido1_LF1_r3-v3step4-vizsuccessyes3740:01:22(hidden)
driven_lanedir_median2.1765223077616915
deviation-center-line_median0.2457179113717786
in-drivable-lane_median0.03333333333333344


other stats
deviation-center-line_max0.2618126260063796
deviation-center-line_mean0.23330309135200375
deviation-center-line_min0.174144338329971
deviation-heading_max0.909368062200488
deviation-heading_mean0.7935006539837912
deviation-heading_median0.7829508049716066
deviation-heading_min0.6680064835149021
driven_any_max2.458592938363241
driven_any_mean2.1351239316271498
driven_any_median2.257286983013766
driven_any_min1.425262184179509
driven_lanedir_max2.2500734762342356
driven_lanedir_mean1.964627050622574
driven_lanedir_min1.1783325417288104
in-drivable-lane_max0.23333333333333345
in-drivable-lane_mean0.06000000000000005
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 2.458592938363241, "driven_lanedir": 2.1781938900330253, "in-drivable-lane": 0.23333333333333345, "deviation-heading": 0.8651783850286463, "deviation-center-line": 0.2457179113717786}, "ep001": {"driven_any": 2.257286983013766, "driven_lanedir": 2.1765223077616915, "in-drivable-lane": 0, "deviation-heading": 0.7419995342033128, "deviation-center-line": 0.25753575779701304}, "ep002": {"driven_any": 2.1566655015948437, "driven_lanedir": 2.040013037355108, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.6680064835149021, "deviation-center-line": 0.22730482325487655}, "ep003": {"driven_any": 2.377812050984386, "driven_lanedir": 2.2500734762342356, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.7829508049716066, "deviation-center-line": 0.2618126260063796}, "ep004": {"driven_any": 1.425262184179509, "driven_lanedir": 1.1783325417288104, "in-drivable-lane": 0, "deviation-heading": 0.909368062200488, "deviation-center-line": 0.174144338329971}}
152521800hvrigazovVisteon perception teamaido1_LF1_r3-v3step1-simulationfailedyes3740:01:36
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 163, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 104, in solve
    img_input = preprocess_img(observation)
  File "solution.py", line 39, in preprocess_img
    pil_image = Image.fromarray(img_int).resize((80, 60))
NameError: global name 'Image' is not defined


(hidden)
152401789BenjaminMy ROS solutionaido1_LF1_r3-v3step3-videossuccessyes3740:01:46(hidden)
other stats
videos1
152121782WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.9098507032245398, "good_angle": 0.4724773991257773, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.7333333333333375}, "ep001": {"nsteps": 500, "reward": 0.9491027700677516, "good_angle": 0.2512812753366602, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.49999999999999833}, "ep002": {"nsteps": 500, "reward": 1.0023943416476249, "good_angle": 0.3554348517254828, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.9000000000000012}, "ep003": {"nsteps": 500, "reward": 0.9913039965033532, "good_angle": 0.2604950315413351, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.33333333333333215}, "ep004": {"nsteps": 500, "reward": 0.998451233804226, "good_angle": 0.36632949005626153, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.6666666666666656}}
good_angle_max0.4724773991257773
good_angle_mean0.3412036095571034
good_angle_median0.3554348517254828
good_angle_min0.2512812753366602
reward_max1.0023943416476249
reward_mean0.9702206090494988
reward_median0.9913039965033532
reward_min0.9098507032245398
survival_time_max16.666666666666654
survival_time_mean16.666666666666654
survival_time_min16.666666666666654
traveled_tiles_max18
traveled_tiles_mean18
traveled_tiles_median18
traveled_tiles_min18
valid_direction_max0.9000000000000012
valid_direction_mean0.626666666666667
valid_direction_median0.6666666666666656
valid_direction_min0.33333333333333215
151961775WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step4-vizsuccessyes3740:04:54(hidden)
driven_lanedir_median17.91468848396697
deviation-center-line_median0.6886453002162244
in-drivable-lane_median0.29999999999999916


other stats
deviation-center-line_max0.7418391533825798
deviation-center-line_mean0.6580369453193626
deviation-center-line_min0.5665967477457616
deviation-heading_max1.7334590674768626
deviation-heading_mean1.6168606854104646
deviation-heading_median1.588613647134545
deviation-heading_min1.5470858923283384
driven_any_max18.693018203387386
driven_any_mean18.491246621280435
driven_any_median18.460680550608178
driven_any_min18.35592902455691
driven_lanedir_max18.1078512569771
driven_lanedir_mean17.93625609629302
driven_lanedir_min17.668173509151664
in-drivable-lane_max0.5333333333333325
in-drivable-lane_mean0.32666666666666605
in-drivable-lane_min0.1666666666666662
per-episodes
details{"ep000": {"driven_any": 18.35592902455691, "driven_lanedir": 17.668173509151664, "in-drivable-lane": 0.46666666666666606, "deviation-heading": 1.588613647134545, "deviation-center-line": 0.5956716052125491}, "ep001": {"driven_any": 18.460680550608178, "driven_lanedir": 18.1078512569771, "in-drivable-lane": 0.1666666666666662, "deviation-heading": 1.575015524879832, "deviation-center-line": 0.5665967477457616}, "ep002": {"driven_any": 18.693018203387386, "driven_lanedir": 17.888535318811854, "in-drivable-lane": 0.5333333333333325, "deviation-heading": 1.5470858923283384, "deviation-center-line": 0.6886453002162244}, "ep003": {"driven_any": 18.44032714457728, "driven_lanedir": 17.91468848396697, "in-drivable-lane": 0.29999999999999916, "deviation-heading": 1.6401292952327444, "deviation-center-line": 0.697431920039698}, "ep004": {"driven_any": 18.506278183272432, "driven_lanedir": 18.102031912557525, "in-drivable-lane": 0.1666666666666664, "deviation-heading": 1.7334590674768626, "deviation-center-line": 0.7418391533825798}}
151901773WEIGAONUS & Panasonic R&D Center Singaporeaido1_LF1_r3-v3step3-videossuccessyes3740:01:45(hidden)
other stats
videos1
151871772mpicquetPython templateaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:47
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 50, in of
    pickup = self.matchClosestTaxi(request, status, index)
  File "/project/src/DispatchingLogic.py", line 64, in matchClosestTaxi
    while index < len(status[1]):
TypeError: object of type 'float' has no len()


(hidden)
151811770ossamaAhmedPython templateaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:02:49(hidden)
other stats
passedtrue
151801769zgxsinPython templateaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:19(hidden)
service_quality-33.65635705414362


other stats
efficiency-65.51786821657464
fleet_size-1000000000
151431760WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LF1_r3-v3step1-simulationsuccessyes3740:06:42(hidden)
other stats
simulation-passed1
151401758WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LFV_r1-v3step4-vizsuccessno3740:04:03(hidden)
deviation-center-line_median0.5426628603674961
in-drivable-lane_median0.06666666666666643


other stats
deviation-center-line_max0.7755980945810605
deviation-center-line_mean0.5035587693199987
deviation-center-line_min0.02541178458093277
deviation-heading_max1.3736979217538725
deviation-heading_mean1.0183098314113386
deviation-heading_median1.238002660378309
deviation-heading_min0.12075495423696668
driven_any_max15.888823328506938
driven_any_mean12.763060709381849
driven_any_median15.879035370997256
driven_any_min0.3093818831851653
driven_lanedir_max15.698849059411932
driven_lanedir_mean12.545096931219394
driven_lanedir_median15.662176360263722
driven_lanedir_min0.2889161068133328
in-drivable-lane_max0.4333333333333333
in-drivable-lane_mean0.12666666666666687
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 15.888823328506938, "driven_lanedir": 15.37881083056834, "in-drivable-lane": 0.4333333333333333, "deviation-heading": 1.050277114224336, "deviation-center-line": 0.5426628603674961}, "ep001": {"driven_any": 15.879035370997256, "driven_lanedir": 15.662176360263722, "in-drivable-lane": 0.0666666666666682, "deviation-heading": 1.3736979217538725, "deviation-center-line": 0.7755980945810605}, "ep002": {"driven_any": 0.3093818831851653, "driven_lanedir": 0.2889161068133328, "in-drivable-lane": 0, "deviation-heading": 0.12075495423696668, "deviation-center-line": 0.02541178458093277}, "ep003": {"driven_any": 15.87925896832069, "driven_lanedir": 15.698849059411932, "in-drivable-lane": 0.06666666666666643, "deviation-heading": 1.238002660378309, "deviation-center-line": 0.6966756183946697}, "ep004": {"driven_any": 15.858803995899184, "driven_lanedir": 15.696732299039638, "in-drivable-lane": 0.06666666666666643, "deviation-heading": 1.3088165064632082, "deviation-center-line": 0.4774454886758346}}
151371758WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LFV_r1-v3step3-videossuccessyes3740:01:52(hidden)
other stats
videos1
151341757WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LF1_r3-v3step1-simulationsuccessyes3740:06:55(hidden)
other stats
simulation-passed1
151331756Liam PaullROS-based Lane Followingaido1_LF1_r3-v3step4-vizsuccessyes3740:00:56(hidden)
driven_lanedir_median0.326541471503506
deviation-center-line_median0.0763742367600679
in-drivable-lane_median0


other stats
deviation-center-line_max0.135174719713286
deviation-center-line_mean0.08709641765910547
deviation-center-line_min0.0291470401357046
deviation-heading_max0.8195512746771606
deviation-heading_mean0.34421510790875703
deviation-heading_median0.23476207628554396
deviation-heading_min0.21013484747400127
driven_any_max0.599572129421278
driven_any_mean0.3555010210013046
driven_any_median0.3747753171281144
driven_any_min0.09627224322032056
driven_lanedir_max0.40630296788669207
driven_lanedir_mean0.2878523133653659
driven_lanedir_min0.06547861466656557
in-drivable-lane_max0.5666666666666667
in-drivable-lane_mean0.11333333333333331
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.599572129421278, "driven_lanedir": 0.326541471503506, "in-drivable-lane": 0.5666666666666667, "deviation-heading": 0.8195512746771606, "deviation-center-line": 0.12222240564088951}, "ep001": {"driven_any": 0.28916083383836433, "driven_lanedir": 0.27887092887232745, "in-drivable-lane": 0, "deviation-heading": 0.21013484747400127, "deviation-center-line": 0.0763742367600679}, "ep002": {"driven_any": 0.3747753171281144, "driven_lanedir": 0.3620675838977383, "in-drivable-lane": 0, "deviation-heading": 0.23476207628554396, "deviation-center-line": 0.07256368604557942}, "ep003": {"driven_any": 0.09627224322032056, "driven_lanedir": 0.06547861466656557, "in-drivable-lane": 0, "deviation-heading": 0.24489691596209173, "deviation-center-line": 0.0291470401357046}, "ep004": {"driven_any": 0.4177245813984453, "driven_lanedir": 0.40630296788669207, "in-drivable-lane": 0, "deviation-heading": 0.21173042514498744, "deviation-center-line": 0.135174719713286}}
151311756Liam PaullROS-based Lane Followingaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median1.2333333333333338


other stats
episodes
details{"ep000": {"nsteps": 58, "reward": -17.196405136315473, "good_angle": 1.1428370734433102, "survival_time": 1.933333333333336, "traveled_tiles": 1, "valid_direction": 1.466666666666668}, "ep001": {"nsteps": 29, "reward": -34.14589061164136, "good_angle": 0.07977230915140875, "survival_time": 0.9666666666666666, "traveled_tiles": 2, "valid_direction": 0.23333333333333328}, "ep002": {"nsteps": 37, "reward": -26.641796510515583, "good_angle": 0.10946592448409403, "survival_time": 1.2333333333333338, "traveled_tiles": 2, "valid_direction": 0.16666666666666707}, "ep003": {"nsteps": 11, "reward": -90.92974479767408, "good_angle": 0.2698276197356042, "survival_time": 0.36666666666666664, "traveled_tiles": 1, "valid_direction": 0.29999999999999993}, "ep004": {"nsteps": 41, "reward": -24.15637273632172, "good_angle": 0.1128055203380938, "survival_time": 1.3666666666666676, "traveled_tiles": 2, "valid_direction": 0.20000000000000065}}
good_angle_max1.1428370734433102
good_angle_mean0.3429416894305022
good_angle_median0.1128055203380938
good_angle_min0.07977230915140875
reward_max-17.196405136315473
reward_mean-38.61404195849365
reward_median-26.641796510515583
reward_min-90.92974479767408
survival_time_max1.933333333333336
survival_time_mean1.1733333333333342
survival_time_min0.36666666666666664
traveled_tiles_max2
traveled_tiles_mean1.6
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max1.466666666666668
valid_direction_mean0.47333333333333377
valid_direction_median0.23333333333333328
valid_direction_min0.16666666666666707
151221754Liam PaullRandom executionaido1_LF1_r3-v3step1-simulationsuccessyes3740:01:03(hidden)
other stats
simulation-passed1
151171753lsieberSimple FIFOaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:03:56(hidden)
other stats
passedtrue
151121749Liam PaullTemplate for ROS Submissionaido1_LF1_r3-v3step4-vizsuccessyes3740:00:56(hidden)
driven_lanedir_median0.5490201560938999
deviation-center-line_median0.09157042741156993
in-drivable-lane_median0.03333333333333344


other stats
deviation-center-line_max0.1175114890343862
deviation-center-line_mean0.07898201424428306
deviation-center-line_min0.0360368736516752
deviation-heading_max0.5233735564281554
deviation-heading_mean0.3874625258901956
deviation-heading_median0.46488952689641383
deviation-heading_min0.1117460160676744
driven_any_max1.194036352981876
driven_any_mean0.7889472759164289
driven_any_median0.9756139477606448
driven_any_min0.2102655071802833
driven_lanedir_max0.8933766211004817
driven_lanedir_mean0.5925001862373207
driven_lanedir_min0.2019177736623772
in-drivable-lane_max0.8666666666666667
in-drivable-lane_mean0.2600000000000001
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.9756139477606448, "driven_lanedir": 0.4279856901093078, "in-drivable-lane": 0.8666666666666667, "deviation-heading": 0.46488952689641383, "deviation-center-line": 0.09157042741156993}, "ep001": {"driven_any": 1.194036352981876, "driven_lanedir": 0.8902006902205364, "in-drivable-lane": 0.40000000000000047, "deviation-heading": 0.5233735564281554, "deviation-center-line": 0.10764695220512756}, "ep002": {"driven_any": 0.579181261215232, "driven_lanedir": 0.5490201560938999, "in-drivable-lane": 0, "deviation-heading": 0.3167174546471054, "deviation-center-line": 0.0421443289186564}, "ep003": {"driven_any": 0.985639310444108, "driven_lanedir": 0.8933766211004817, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.5205860754116288, "deviation-center-line": 0.1175114890343862}, "ep004": {"driven_any": 0.2102655071802833, "driven_lanedir": 0.2019177736623772, "in-drivable-lane": 0, "deviation-heading": 0.1117460160676744, "deviation-center-line": 0.0360368736516752}}
151101749Liam PaullTemplate for ROS Submissionaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:25(hidden)
survival_time_median1.6666666666666683


other stats
episodes
details{"ep000": {"nsteps": 55, "reward": -18.43541585992683, "good_angle": 0.5860453747406704, "survival_time": 1.8333333333333357, "traveled_tiles": 2, "valid_direction": 1.100000000000002}, "ep001": {"nsteps": 63, "reward": -15.140283202604644, "good_angle": 0.2189546991927596, "survival_time": 2.1000000000000023, "traveled_tiles": 3, "valid_direction": 0.5333333333333343}, "ep002": {"nsteps": 32, "reward": -30.67577763623558, "good_angle": 0.1857032553602409, "survival_time": 1.0666666666666669, "traveled_tiles": 1, "valid_direction": 0.33333333333333337}, "ep003": {"nsteps": 50, "reward": -19.59453119471669, "good_angle": 0.4578149542398154, "survival_time": 1.6666666666666683, "traveled_tiles": 2, "valid_direction": 0.8666666666666686}, "ep004": {"nsteps": 14, "reward": -71.11600483129067, "good_angle": 0.039963763859264305, "survival_time": 0.4666666666666666, "traveled_tiles": 1, "valid_direction": 0.09999999999999998}}
good_angle_max0.5860453747406704
good_angle_mean0.2976964094785501
good_angle_median0.2189546991927596
good_angle_min0.039963763859264305
reward_max-15.140283202604644
reward_mean-30.99240254495488
reward_median-19.59453119471669
reward_min-71.11600483129067
survival_time_max2.1000000000000023
survival_time_mean1.426666666666668
survival_time_min0.4666666666666666
traveled_tiles_max3
traveled_tiles_mean1.8
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max1.100000000000002
valid_direction_mean0.5866666666666677
valid_direction_median0.5333333333333343
valid_direction_min0.09999999999999998
151081748lsieberSimple FIFOaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:18
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 71, in of
    rebal = self.getClosestRoboTaxi(request, rabaltaxis)
  File "/project/src/DispatchingLogic.py", line 99, in getClosestRoboTaxi
    lon = request[2][0]
TypeError: 'int' object has no attribute '__getitem__'


(hidden)
151061746Liam PaullTensorflow templateaido1_LF1_r3-v3step4-vizsuccessyes3740:02:47(hidden)
driven_lanedir_median5.302566481656299
deviation-center-line_median0.5565671916823862
in-drivable-lane_median0.16666666666666652


other stats
deviation-center-line_max0.8859936557711611
deviation-center-line_mean0.528607812747053
deviation-center-line_min0.16606102536010822
deviation-heading_max3.033370854841319
deviation-heading_mean2.280705656084282
deviation-heading_median2.4561990578057573
deviation-heading_min0.9882049312556496
driven_any_max9.0481182555653
driven_any_mean6.141090110593249
driven_any_median5.90889969385339
driven_any_min1.2967673217013216
driven_lanedir_max8.21557018025004
driven_lanedir_mean5.398798112513221
driven_lanedir_min1.1127930583588117
in-drivable-lane_max1.433333333333331
in-drivable-lane_mean0.5466666666666657
in-drivable-lane_min0.03333333333333344
per-episodes
details{"ep000": {"driven_any": 5.90889969385339, "driven_lanedir": 4.810083815920299, "in-drivable-lane": 0.9666666666666656, "deviation-heading": 2.4510219442237524, "deviation-center-line": 0.44833422712128373}, "ep001": {"driven_any": 9.0481182555653, "driven_lanedir": 7.552977026380654, "in-drivable-lane": 1.433333333333331, "deviation-heading": 2.4747314922949313, "deviation-center-line": 0.5565671916823862}, "ep002": {"driven_any": 8.688836269603568, "driven_lanedir": 8.21557018025004, "in-drivable-lane": 0.13333333333333308, "deviation-heading": 3.033370854841319, "deviation-center-line": 0.8859936557711611}, "ep003": {"driven_any": 5.762829012242665, "driven_lanedir": 5.302566481656299, "in-drivable-lane": 0.16666666666666652, "deviation-heading": 2.4561990578057573, "deviation-center-line": 0.5860829638003254}, "ep004": {"driven_any": 1.2967673217013216, "driven_lanedir": 1.1127930583588117, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.9882049312556496, "deviation-center-line": 0.16606102536010822}}
151051746Liam PaullTensorflow templateaido1_LF1_r3-v3step3-videossuccessyes3740:01:03(hidden)
other stats
videos1
151021746Liam PaullTensorflow templateaido1_LF1_r3-v3step1-simulationsuccessyes3740:02:35(hidden)
other stats
simulation-passed1
150971744JonJP pipelineaido1_LF1_r3-v3step1-simulationsuccessyes3740:06:48(hidden)
other stats
simulation-passed1
150921742Liam PaullRandom executionaido1_LF1_r3-v3step4-vizsuccessyes3740:01:03(hidden)
driven_lanedir_median0.7282056417234322
deviation-center-line_median0.08936238714188884
in-drivable-lane_median0.03333333333333344


other stats
deviation-center-line_max0.26197781834529354
deviation-center-line_mean0.11324861928264791
deviation-center-line_min0.05212408887016973
deviation-heading_max0.7308539310959989
deviation-heading_mean0.4142795941798942
deviation-heading_median0.3890836965727039
deviation-heading_min0.12159451383651812
driven_any_max1.3202333678660911
driven_any_mean0.9534811732287288
driven_any_median1.1093351302576198
driven_any_min0.40595842043625274
driven_lanedir_max1.2658457216642818
driven_lanedir_mean0.7053365096565809
driven_lanedir_min0.042003314954072835
in-drivable-lane_max2.1666666666666687
in-drivable-lane_mean0.46666666666666695
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.1093351302576198, "driven_lanedir": 0.042003314954072835, "in-drivable-lane": 2.1666666666666687, "deviation-heading": 0.7308539310959989, "deviation-center-line": 0.05212408887016973}, "ep001": {"driven_any": 1.3202333678660911, "driven_lanedir": 1.2658457216642818, "in-drivable-lane": 0.09999999999999964, "deviation-heading": 0.32340891735409777, "deviation-center-line": 0.26197781834529354}, "ep002": {"driven_any": 0.7814833832064715, "driven_lanedir": 0.7282056417234322, "in-drivable-lane": 0.033333333333333326, "deviation-heading": 0.3890836965727039, "deviation-center-line": 0.05781963917554586}, "ep003": {"driven_any": 0.40595842043625274, "driven_lanedir": 0.4026932204807736, "in-drivable-lane": 0, "deviation-heading": 0.12159451383651812, "deviation-center-line": 0.08936238714188884}, "ep004": {"driven_any": 1.1503955643772088, "driven_lanedir": 1.087934649460344, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.506456912040152, "deviation-center-line": 0.10495916288034172}}
150891742Liam PaullRandom executionaido1_LF1_r3-v3step1-simulationsuccessyes3740:01:20(hidden)
other stats
simulation-passed1
150881741Liam PaullTemplate for ROS Submissionaido1_LF1_r3-v3step4-vizsuccessyes3740:00:52(hidden)
driven_lanedir_median0.3654875812706151
deviation-center-line_median0.05199921301450515
in-drivable-lane_median0


other stats
deviation-center-line_max0.12257121256759604
deviation-center-line_mean0.06994296526154051
deviation-center-line_min0.023360625314385673
deviation-heading_max0.6232669324900013
deviation-heading_mean0.3642696064541133
deviation-heading_median0.31518405047622317
deviation-heading_min0.12784378742952962
driven_any_max1.177057265866121
driven_any_mean0.7689653001654593
driven_any_median1.0265497730358817
driven_any_min0.18086623334742635
driven_lanedir_max1.0005584700183718
driven_lanedir_mean0.460295566566745
driven_lanedir_min0.05053788919476376
in-drivable-lane_max1.3666666666666676
in-drivable-lane_mean0.40000000000000047
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.0265497730358817, "driven_lanedir": 0.05053788919476376, "in-drivable-lane": 1.3666666666666676, "deviation-heading": 0.31518405047622317, "deviation-center-line": 0.023360625314385673}, "ep001": {"driven_any": 0.18086623334742635, "driven_lanedir": 0.16669385392295055, "in-drivable-lane": 0, "deviation-heading": 0.12784378742952962, "deviation-center-line": 0.03196321615685817}, "ep002": {"driven_any": 1.0797688161140009, "driven_lanedir": 1.0005584700183718, "in-drivable-lane": 0, "deviation-heading": 0.6232669324900013, "deviation-center-line": 0.11982055925435744}, "ep003": {"driven_any": 1.177057265866121, "driven_lanedir": 0.7182000384270235, "in-drivable-lane": 0.6333333333333346, "deviation-heading": 0.5746084524435707, "deviation-center-line": 0.12257121256759604}, "ep004": {"driven_any": 0.3805844124638672, "driven_lanedir": 0.3654875812706151, "in-drivable-lane": 0, "deviation-heading": 0.18044480943124153, "deviation-center-line": 0.05199921301450515}}
150781739WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.9347234551869332, "good_angle": 0.3942856261665689, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.6666666666666657}, "ep001": {"nsteps": 500, "reward": 0.9512608353495596, "good_angle": 0.16338368474850873, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.09999999999999964}, "ep002": {"nsteps": 500, "reward": 1.0149583086967469, "good_angle": 0.300614737231154, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.6000000000000071}, "ep003": {"nsteps": 500, "reward": 1.0169171295166015, "good_angle": 0.20625552309044684, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.23333333333333273}, "ep004": {"nsteps": 500, "reward": 0.9949625515937806, "good_angle": 0.2993219655676049, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.49999999999999867}}
good_angle_max0.3942856261665689
good_angle_mean0.27277230736085667
good_angle_median0.2993219655676049
good_angle_min0.16338368474850873
reward_max1.0169171295166015
reward_mean0.9825644560687244
reward_median0.9949625515937806
reward_min0.9347234551869332
survival_time_max16.666666666666654
survival_time_mean16.666666666666654
survival_time_min16.666666666666654
traveled_tiles_max18
traveled_tiles_mean18
traveled_tiles_median18
traveled_tiles_min18
valid_direction_max0.6666666666666657
valid_direction_mean0.4200000000000008
valid_direction_median0.49999999999999867
valid_direction_min0.09999999999999964
150751737lsieberSimple FIFOaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:18
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 65, in of
    rebalance.append([rebal[0], request[2]])
TypeError: 'NoneType' object has no attribute '__getitem__'


(hidden)
150731735lsieberSimple FIFOaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:19(hidden)
service_quality-21.992747657522855


other stats
efficiency-41.79491063008979
fleet_size-1000000000
150711734lsieberSimple FIFOaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:18(hidden)
service_quality-113.42745491306012


other stats
efficiency-129.1609396522413
fleet_size-1000000000
150661731lsieberSimple FIFOaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:22
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 53, in of
    robotaxi = self.getClosestRoboTaxi(request, robotaxis, matchedTax)
  File "/project/src/DispatchingLogic.py", line 87, in getClosestRoboTaxi
    if closestDistance > distance and roboTaxi not in matchedRoboTaxis:
TypeError: unhashable type: 'list'


(hidden)
150551725miksazJetBrains Researchaido1_LF1_r3-v3step4-vizsuccessyes3740:04:13(hidden)
driven_lanedir_median17.975254034500537
deviation-center-line_median1.1312672979209517
in-drivable-lane_median0.4000000000000007


other stats
deviation-center-line_max1.16417013174681
deviation-center-line_mean1.0259002769400556
deviation-center-line_min0.539597820405541
deviation-heading_max3.0005954638217136
deviation-heading_mean2.5164679349756627
deviation-heading_median2.781525914961212
deviation-heading_min1.4413239949469432
driven_any_max19.16437780092431
driven_any_mean16.850281187861235
driven_any_median18.91786439403724
driven_any_min8.167086283693928
driven_lanedir_max18.3194912324776
driven_lanedir_mean15.954056351875948
driven_lanedir_min7.552026118934883
in-drivable-lane_max0.5333333333333334
in-drivable-lane_mean0.4133333333333328
in-drivable-lane_min0.2999999999999994
per-episodes
details{"ep000": {"driven_any": 19.159136573207828, "driven_lanedir": 18.3194912324776, "in-drivable-lane": 0.3999999999999987, "deviation-heading": 2.4918270933504587, "deviation-center-line": 1.1307899014010203}, "ep001": {"driven_any": 19.16437780092431, "driven_lanedir": 18.07854649238018, "in-drivable-lane": 0.5333333333333334, "deviation-heading": 2.781525914961212, "deviation-center-line": 1.1312672979209517}, "ep002": {"driven_any": 18.84294088744286, "driven_lanedir": 17.84496388108654, "in-drivable-lane": 0.4333333333333318, "deviation-heading": 3.0005954638217136, "deviation-center-line": 1.1636762332259547}, "ep003": {"driven_any": 18.91786439403724, "driven_lanedir": 17.975254034500537, "in-drivable-lane": 0.4000000000000007, "deviation-heading": 2.8670672077979873, "deviation-center-line": 1.16417013174681}, "ep004": {"driven_any": 8.167086283693928, "driven_lanedir": 7.552026118934883, "in-drivable-lane": 0.2999999999999994, "deviation-heading": 1.4413239949469432, "deviation-center-line": 0.539597820405541}}
150541725miksazJetBrains Researchaido1_LF1_r3-v3step3-videossuccessyes3740:01:42(hidden)
other stats
videos1
150531725miksazJetBrains Researchaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.6842721123695373, "good_angle": 0.2512673713605895, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.23333333333333264}, "ep001": {"nsteps": 500, "reward": 0.6948838782384992, "good_angle": 0.32224673980256735, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.16666666666666607}, "ep002": {"nsteps": 500, "reward": 0.7190919768214226, "good_angle": 0.4345237483007988, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.3999999999999987}, "ep003": {"nsteps": 500, "reward": 0.7028048617020249, "good_angle": 0.4055374130096054, "survival_time": 16.666666666666654, "traveled_tiles": 18, "valid_direction": 0.30000000000000027}, "ep004": {"nsteps": 223, "reward": -3.8820973538163, "good_angle": 0.9796538477955076, "survival_time": 7.433333333333317, "traveled_tiles": 14, "valid_direction": 0.5999999999999992}}
good_angle_max0.9796538477955076
good_angle_mean0.4786458240538137
good_angle_median0.4055374130096054
good_angle_min0.2512673713605895
reward_max0.7190919768214226
reward_mean-0.2162089049369632
reward_median0.6948838782384992
reward_min-3.8820973538163
survival_time_max16.666666666666654
survival_time_mean14.819999999999988
survival_time_min7.433333333333317
traveled_tiles_max18
traveled_tiles_mean17.2
traveled_tiles_median18
traveled_tiles_min14
valid_direction_max0.5999999999999992
valid_direction_mean0.33999999999999936
valid_direction_median0.30000000000000027
valid_direction_min0.16666666666666607
150501724lsieberSimple FIFOaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:03
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 53, in of
    robotaxi = self.getClosestRoboTaxi(request, robotaxis, matchedTax)
  File "/project/src/DispatchingLogic.py", line 85, in getClosestRoboTaxi
    roboTaxi = robotaxis[index]
NameError: global name 'robotaxis' is not defined


(hidden)
150491723WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LFV_r1-v3step4-vizsuccessno3740:00:56(hidden)
deviation-center-line_median0.028474263604339804
in-drivable-lane_median0.36666666666666664


other stats
deviation-center-line_max0.24440582549543627
deviation-center-line_mean0.08931159380261612
deviation-center-line_min0.000713222609664843
deviation-heading_max1.7135262777266584
deviation-heading_mean0.5570892119487969
deviation-heading_median0.16702575067657022
deviation-heading_min0.06449231729345228
driven_any_max2.5186617566511083
driven_any_mean0.9308341901525083
driven_any_median0.32305938972351805
driven_any_min0.24359268965133013
driven_lanedir_max0.5344433939918236
driven_lanedir_mean0.1708748305772493
driven_lanedir_median0.05553156400297965
driven_lanedir_min-0.011153021234366234
in-drivable-lane_max1.7999999999999985
in-drivable-lane_mean0.74
in-drivable-lane_min0.16666666666666669
per-episodes
details{"ep000": {"driven_any": 1.284878832152591, "driven_lanedir": 0.2367333864773169, "in-drivable-lane": 1.0333333333333348, "deviation-heading": 0.7078368925487328, "deviation-center-line": 0.1654269127045526}, "ep001": {"driven_any": 0.32305938972351805, "driven_lanedir": 0.03881882964849259, "in-drivable-lane": 0.36666666666666664, "deviation-heading": 0.1325648214985705, "deviation-center-line": 0.007537744599087107}, "ep002": {"driven_any": 0.24359268965133013, "driven_lanedir": -0.011153021234366234, "in-drivable-lane": 0.33333333333333326, "deviation-heading": 0.06449231729345228, "deviation-center-line": 0.000713222609664843}, "ep003": {"driven_any": 0.28397828258399466, "driven_lanedir": 0.05553156400297965, "in-drivable-lane": 0.16666666666666669, "deviation-heading": 0.16702575067657022, "deviation-center-line": 0.028474263604339804}, "ep004": {"driven_any": 2.5186617566511083, "driven_lanedir": 0.5344433939918236, "in-drivable-lane": 1.7999999999999985, "deviation-heading": 1.7135262777266584, "deviation-center-line": 0.24440582549543627}}
150241709WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LFV_r1-v3step2-scoringsuccessyes3740:01:31(hidden)
survival_time_median0.6666666666666666


other stats
episodes
details{"ep000": {"nsteps": 26, "reward": -39.084675945556505, "good_angle": 0.817122339543548, "survival_time": 0.8666666666666666, "traveled_tiles": 1, "valid_direction": 0.7666666666666666}, "ep001": {"nsteps": 20, "reward": -51.50990334451198, "good_angle": 0.5749231473187927, "survival_time": 0.6666666666666666, "traveled_tiles": 1, "valid_direction": 0.5333333333333332}, "ep002": {"nsteps": 22, "reward": -46.42071894522417, "good_angle": 0.7123052109464987, "survival_time": 0.7333333333333333, "traveled_tiles": 1, "valid_direction": 0.6}, "ep003": {"nsteps": 11, "reward": -91.06625051694836, "good_angle": 0.3044758038672967, "survival_time": 0.36666666666666664, "traveled_tiles": 2, "valid_direction": 0.2666666666666666}, "ep004": {"nsteps": 13, "reward": -76.69704971920986, "good_angle": 0.14050482492472005, "survival_time": 0.4333333333333333, "traveled_tiles": 1, "valid_direction": 0.2666666666666666}}
good_angle_max0.817122339543548
good_angle_mean0.5098662653201712
good_angle_median0.5749231473187927
good_angle_min0.14050482492472005
reward_max-39.084675945556505
reward_mean-60.95571969429018
reward_median-51.50990334451198
reward_min-91.06625051694836
survival_time_max0.8666666666666666
survival_time_mean0.6133333333333333
survival_time_min0.36666666666666664
traveled_tiles_max2
traveled_tiles_mean1.2
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max0.7666666666666666
valid_direction_mean0.48666666666666664
valid_direction_median0.5333333333333332
valid_direction_min0.2666666666666666
150211707WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LFV_r1-v3step4-vizsuccessno3740:03:27(hidden)
deviation-center-line_median0.20072603781464896
in-drivable-lane_median0.06666666666666643


other stats
deviation-center-line_max0.3498845818921222
deviation-center-line_mean0.21996384646265368
deviation-center-line_min0.09190813933426528
deviation-heading_max3.809070624505954
deviation-heading_mean2.1867568435391993
deviation-heading_median2.1644518961780035
deviation-heading_min0.2858781416498939
driven_any_max10.31032552095146
driven_any_mean5.8455961774137375
driven_any_median6.2652045066489945
driven_any_min0.6315943051633945
driven_lanedir_max8.233227074448305
driven_lanedir_mean4.624992192496739
driven_lanedir_median4.879204421606125
driven_lanedir_min0.5051585641380438
in-drivable-lane_max0.2
in-drivable-lane_mean0.07999999999999986
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 8.322965580522789, "driven_lanedir": 6.445647357588332, "in-drivable-lane": 0.2, "deviation-heading": 3.328974261382753, "deviation-center-line": 0.3498845818921222}, "ep001": {"driven_any": 6.2652045066489945, "driven_lanedir": 4.879204421606125, "in-drivable-lane": 0.09999999999999964, "deviation-heading": 2.1644518961780035, "deviation-center-line": 0.20072603781464896}, "ep002": {"driven_any": 3.697890973782051, "driven_lanedir": 3.0617235447028928, "in-drivable-lane": 0.06666666666666643, "deviation-heading": 1.345409293979393, "deviation-center-line": 0.11104521736883348}, "ep003": {"driven_any": 0.6315943051633945, "driven_lanedir": 0.5051585641380438, "in-drivable-lane": 0, "deviation-heading": 0.2858781416498939, "deviation-center-line": 0.09190813933426528}, "ep004": {"driven_any": 10.31032552095146, "driven_lanedir": 8.233227074448305, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 3.809070624505954, "deviation-center-line": 0.34625525590339845}}
150071704WEIGAONUS&PANASONIC_R&D_SINGAPOREaido1_LF1_r3-v3step3-videossuccessyes3740:04:21(hidden)
other stats
videos1
150031702BenjaminMy ROS solutionaido1_LF1_r3-v3step4-vizsuccessyes3740:00:55(hidden)
driven_lanedir_median0.6633896256650185
deviation-center-line_median0.05640867162505691
in-drivable-lane_median0


other stats
deviation-center-line_max0.07004183574910067
deviation-center-line_mean0.05702080401039258
deviation-center-line_min0.04319521801849021
deviation-heading_max0.2760399411250118
deviation-heading_mean0.1825000331010141
deviation-heading_median0.19580967241582095
deviation-heading_min0.07573406376169306
driven_any_max1.1334753172268193
driven_any_mean0.8666408111611681
driven_any_median0.8371503888906588
driven_any_min0.7282160856766959
driven_lanedir_max1.114709974568877
driven_lanedir_mean0.718091586600886
driven_lanedir_min0.41426131503797714
in-drivable-lane_max0.3333333333333333
in-drivable-lane_mean0.1
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 0.8371503888906588, "driven_lanedir": 0.41426131503797714, "in-drivable-lane": 0.3333333333333333, "deviation-heading": 0.19580967241582095, "deviation-center-line": 0.04319521801849021}, "ep001": {"driven_any": 0.7282160856766959, "driven_lanedir": 0.6633896256650185, "in-drivable-lane": 0, "deviation-heading": 0.2760399411250118, "deviation-center-line": 0.05640867162505691}, "ep002": {"driven_any": 0.792371161443559, "driven_lanedir": 0.5647090310556504, "in-drivable-lane": 0.16666666666666663, "deviation-heading": 0.23043220503431425, "deviation-center-line": 0.05077399157122318}, "ep003": {"driven_any": 0.8419911025681075, "driven_lanedir": 0.8333879866769066, "in-drivable-lane": 0, "deviation-heading": 0.07573406376169306, "deviation-center-line": 0.0646843030880919}, "ep004": {"driven_any": 1.1334753172268193, "driven_lanedir": 1.114709974568877, "in-drivable-lane": 0, "deviation-heading": 0.13448428316823038, "deviation-center-line": 0.07004183574910067}}
149801686JonJP pipelineaido1_LF1_r3-v3step4-vizsuccessyes3740:05:02(hidden)
driven_lanedir_median16.335574011879036
deviation-center-line_median1.0444064856536095
in-drivable-lane_median1.6000000000000056


other stats
deviation-center-line_max1.1199208746741478
deviation-center-line_mean0.9034644580589164
deviation-center-line_min0.6475055535490419
deviation-heading_max2.4897497282854237
deviation-heading_mean2.001625775234914
deviation-heading_median2.435186744265479
deviation-heading_min1.160105984477231
driven_any_max19.08644144337508
driven_any_mean15.764722872947305
driven_any_median18.95343719128836
driven_any_min10.614911570017236
driven_lanedir_max16.63344870405414
driven_lanedir_mean13.574702733558876
driven_lanedir_min8.697876190248273
in-drivable-lane_max1.9666666666666763
in-drivable-lane_mean1.6200000000000003
in-drivable-lane_min1.099999999999996
per-episodes
details{"ep000": {"driven_any": 10.614911570017236, "driven_lanedir": 8.697876190248273, "in-drivable-lane": 1.4999999999999964, "deviation-heading": 1.4354348009897515, "deviation-center-line": 0.6475055535490419}, "ep001": {"driven_any": 19.04554329870867, "driven_lanedir": 16.436320008197605, "in-drivable-lane": 1.933333333333328, "deviation-heading": 2.435186744265479, "deviation-center-line": 1.0444064856536095}, "ep002": {"driven_any": 19.08644144337508, "driven_lanedir": 16.335574011879036, "in-drivable-lane": 1.9666666666666763, "deviation-heading": 2.4876516181566832, "deviation-center-line": 1.057638901024734}, "ep003": {"driven_any": 11.123280861347173, "driven_lanedir": 9.770294753415332, "in-drivable-lane": 1.099999999999996, "deviation-heading": 1.160105984477231, "deviation-center-line": 0.6478504753930472}, "ep004": {"driven_any": 18.95343719128836, "driven_lanedir": 16.63344870405414, "in-drivable-lane": 1.6000000000000056, "deviation-heading": 2.4897497282854237, "deviation-center-line": 1.1199208746741478}}
149791685JonJP pipelineaido1_LF1_r3-v3step3-videossuccessyes3740:02:26(hidden)
other stats
videos1
149581683zjdongSolution templateaido1_luck-v3step1successyes3740:00:39(hidden)
score161.65875153377712


149541682Andreas Aumiller simple redispatcheraido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:03:41(hidden)
other stats
passedtrue
149511678rumpelstilzchenM876xzaido1_amod_fleet_size_r1-v3step1-simulationerroryes3740:30:45
Timeout: Waited 180 [...]
Timeout:

Waited 1802.92606282 for container to finish. Giving up. 
(hidden)
149371671rumpelstilzchenSimsalabimaido1_amod_fleet_size_r1-v3step2-scoringsuccessyes3740:00:15(hidden)
fleet_size-1000000000


other stats
efficiency-64.62316443510858
service_quality-33.212851108777045
149361672rumpelstilzchenSimsalabimaido1_amod_fleet_size_r1-v3step2-scoringsuccessyes3740:00:15(hidden)
fleet_size-1000000000


other stats
efficiency-72.18315077107117
service_quality-34.623857692767636
149161666rumpelstilzchenSimsalabimaido1_amod_fleet_size_r1-v3step2-scoringsuccessyes3740:00:18(hidden)
fleet_size-1000000000


other stats
efficiency-72.7787818999971
service_quality-34.80240547499927
149111661miksazJetBrains Researchaido1_LF1_r3-v3step1-simulationsuccessyes3740:04:31(hidden)
other stats
simulation-passed1
149051656rumpelstilzchenMein zweiter Zauberspruchaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:18(hidden)
service_quality-34.45579607744862


other stats
efficiency-71.81718430979495
fleet_size-1000000000
148821647hvrigazovVisteon perception teamaido1_LF1_r3-v3step3-videossuccessyes3740:01:42(hidden)
other stats
videos1
148811647hvrigazovVisteon perception teamaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.4010326168611646, "good_angle": 8.341834391759656, "survival_time": 16.666666666666654, "traveled_tiles": 9, "valid_direction": 4.03333333333334}, "ep001": {"nsteps": 500, "reward": 0.4779691570997238, "good_angle": 1.9476689378587195, "survival_time": 16.666666666666654, "traveled_tiles": 11, "valid_direction": 3.4666666666666623}, "ep002": {"nsteps": 500, "reward": 0.6558319092392921, "good_angle": 0.6505253058643319, "survival_time": 16.666666666666654, "traveled_tiles": 13, "valid_direction": 0.9666666666666648}, "ep003": {"nsteps": 404, "reward": -1.936633684692298, "good_angle": 3.4936946923553696, "survival_time": 13.46666666666663, "traveled_tiles": 9, "valid_direction": 2.299999999999993}, "ep004": {"nsteps": 135, "reward": -7.479596372666182, "good_angle": 5.147318534481444, "survival_time": 4.499999999999994, "traveled_tiles": 3, "valid_direction": 1.999999999999994}}
good_angle_max8.341834391759656
good_angle_mean3.916208372463904
good_angle_median3.4936946923553696
good_angle_min0.6505253058643319
reward_max0.6558319092392921
reward_mean-1.57627927483166
reward_median0.4010326168611646
reward_min-7.479596372666182
survival_time_max16.666666666666654
survival_time_mean13.593333333333316
survival_time_min4.499999999999994
traveled_tiles_max13
traveled_tiles_mean9
traveled_tiles_median9
traveled_tiles_min3
valid_direction_max4.03333333333334
valid_direction_mean2.553333333333331
valid_direction_median2.299999999999993
valid_direction_min0.9666666666666648
148781647hvrigazovVisteon perception teamaido1_LF1_r3-v3step1-simulationsuccessyes3740:04:28(hidden)
other stats
simulation-passed1
148771646WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationsuccessyes3740:06:56(hidden)
other stats
simulation-passed1
148731645WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationsuccessyes3740:05:48(hidden)
other stats
simulation-passed1
148721644WEIGAOFirst trialaido1_LF1_r3-v3step4-vizsuccessyes3740:04:06(hidden)
driven_lanedir_median17.95812783527904
deviation-center-line_median0.5769087824220316
in-drivable-lane_median0.10000000000000032


other stats
deviation-center-line_max0.7812354688107352
deviation-center-line_mean0.4254349143793508
deviation-center-line_min0.023554634305975885
deviation-heading_max1.7418408667191465
deviation-heading_mean1.0930815818981598
deviation-heading_median1.5575377311196614
deviation-heading_min0.21031837683942475
driven_any_max18.766954579753328
driven_any_mean11.631679784907831
driven_any_median18.71260070625704
driven_any_min0.7522654451124056
driven_lanedir_max18.445804987944516
driven_lanedir_mean11.280022768865395
driven_lanedir_min0.6884796727348207
in-drivable-lane_max0.4666666666666663
in-drivable-lane_mean0.17333333333333314
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 18.71260070625704, "driven_lanedir": 17.95812783527904, "in-drivable-lane": 0.4666666666666663, "deviation-heading": 1.6906407004593182, "deviation-center-line": 0.6828461168242704}, "ep001": {"driven_any": 18.76384117045217, "driven_lanedir": 18.445804987944516, "in-drivable-lane": 0.09999999999999976, "deviation-heading": 1.5575377311196614, "deviation-center-line": 0.5769087824220316}, "ep002": {"driven_any": 0.7522654451124056, "driven_lanedir": 0.6884796727348207, "in-drivable-lane": 0, "deviation-heading": 0.21031837683942475, "deviation-center-line": 0.023554634305975885}, "ep003": {"driven_any": 18.766954579753328, "driven_lanedir": 18.300516019127546, "in-drivable-lane": 0.1999999999999994, "deviation-heading": 1.7418408667191465, "deviation-center-line": 0.7812354688107352}, "ep004": {"driven_any": 1.1627370229642102, "driven_lanedir": 1.0071853292410593, "in-drivable-lane": 0.10000000000000032, "deviation-heading": 0.2650702343532486, "deviation-center-line": 0.06262956953374141}}
148671643WEIGAOFirst trialaido1_LF1_r3-v3step4-vizsuccessyes3740:06:29(hidden)
driven_lanedir_median18.042124065546613
deviation-center-line_median0.6920868789014993
in-drivable-lane_median0.1666666666666663


other stats
deviation-center-line_max0.7450422530313244
deviation-center-line_mean0.6626174079534471
deviation-center-line_min0.5413260138801287
deviation-heading_max1.6608467741274584
deviation-heading_mean1.609760609290257
deviation-heading_median1.6249639021565303
deviation-heading_min1.524158966193354
driven_any_max18.560192268938735
driven_any_mean18.49766369215716
driven_any_median18.53851648483881
driven_any_min18.396858497933277
driven_lanedir_max18.21279376278902
driven_lanedir_mean18.07231742667527
driven_lanedir_min17.881025386274594
in-drivable-lane_max0.3333333333333331
in-drivable-lane_mean0.20666666666666633
in-drivable-lane_min0.13333333333333286
per-episodes
details{"ep000": {"driven_any": 18.43462421038289, "driven_lanedir": 17.881025386274594, "in-drivable-lane": 0.3333333333333331, "deviation-heading": 1.6452224666103052, "deviation-center-line": 0.5900540285483239}, "ep001": {"driven_any": 18.396858497933277, "driven_lanedir": 18.038689903432346, "in-drivable-lane": 0.1666666666666663, "deviation-heading": 1.524158966193354, "deviation-center-line": 0.5413260138801287}, "ep002": {"driven_any": 18.560192268938735, "driven_lanedir": 18.042124065546613, "in-drivable-lane": 0.26666666666666633, "deviation-heading": 1.6608467741274584, "deviation-center-line": 0.7445778654059592}, "ep003": {"driven_any": 18.53851648483881, "driven_lanedir": 18.21279376278902, "in-drivable-lane": 0.13333333333333308, "deviation-heading": 1.5936109373636365, "deviation-center-line": 0.6920868789014993}, "ep004": {"driven_any": 18.55812699869209, "driven_lanedir": 18.18695401533378, "in-drivable-lane": 0.13333333333333286, "deviation-heading": 1.6249639021565303, "deviation-center-line": 0.7450422530313244}}
148531640WEIGAOFirst trialaido1_LF1_r3-v3step4-vizsuccessyes3740:04:43(hidden)
driven_lanedir_median17.527038232165747
deviation-center-line_median0.5823445318382586
in-drivable-lane_median0.23333333333333264


other stats
deviation-center-line_max0.7068632863526982
deviation-center-line_mean0.4658843771827443
deviation-center-line_min0.054078672909690435
deviation-heading_max2.1879925353213125
deviation-heading_mean1.3490698303677848
deviation-heading_median1.604857067702707
deviation-heading_min0.3208034642712458
driven_any_max18.52360438320037
driven_any_mean13.075662484082644
driven_any_median18.22263138882232
driven_any_min0.8010482579993252
driven_lanedir_max18.04673244319959
driven_lanedir_mean12.638943323932873
driven_lanedir_min0.6564448346552887
in-drivable-lane_max0.3999999999999996
in-drivable-lane_mean0.21999999999999953
in-drivable-lane_min0.06666666666666665
per-episodes
details{"ep000": {"driven_any": 18.403476813827503, "driven_lanedir": 17.79845520017333, "in-drivable-lane": 0.3999999999999996, "deviation-heading": 1.604857067702707, "deviation-center-line": 0.5823445318382586}, "ep001": {"driven_any": 9.427551576563708, "driven_lanedir": 9.166045909470409, "in-drivable-lane": 0.09999999999999976, "deviation-heading": 0.9614080526558724, "deviation-center-line": 0.29528270380882526}, "ep002": {"driven_any": 0.8010482579993252, "driven_lanedir": 0.6564448346552887, "in-drivable-lane": 0.06666666666666665, "deviation-heading": 0.3208034642712458, "deviation-center-line": 0.054078672909690435}, "ep003": {"driven_any": 18.52360438320037, "driven_lanedir": 18.04673244319959, "in-drivable-lane": 0.23333333333333264, "deviation-heading": 1.670288031887787, "deviation-center-line": 0.6908526910042488}, "ep004": {"driven_any": 18.22263138882232, "driven_lanedir": 17.527038232165747, "in-drivable-lane": 0.29999999999999905, "deviation-heading": 2.1879925353213125, "deviation-center-line": 0.7068632863526982}}
148501639WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationsuccessyes3740:07:09(hidden)
other stats
simulation-passed1
148471638WEIGAOFirst trialaido1_LF1_r3-v3step3-videossuccessyes3740:01:57(hidden)
other stats
videos1
148441637WEIGAOFirst trialaido1_LF1_r3-v3step4-vizsuccessyes3740:04:34(hidden)
driven_lanedir_median17.905793462379442
deviation-center-line_median0.6411577363170983
in-drivable-lane_median0.033333333333333215


other stats
deviation-center-line_max0.7871744447129276
deviation-center-line_mean0.5035283662704194
deviation-center-line_min0.015365374787591144
deviation-heading_max1.8709080152054935
deviation-heading_mean1.3094655026085051
deviation-heading_median1.7021940189909166
deviation-heading_min0.13520363354596854
driven_any_max18.696776827343896
driven_any_mean13.133974500671282
driven_any_median18.5743559079367
driven_any_min0.29988041084327915
driven_lanedir_max18.396904358210985
driven_lanedir_mean12.842551516504017
driven_lanedir_min0.2684917204056503
in-drivable-lane_max0.39999999999999974
in-drivable-lane_mean0.10666666666666656
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 18.5743559079367, "driven_lanedir": 17.905793462379442, "in-drivable-lane": 0.39999999999999974, "deviation-heading": 1.748607173195759, "deviation-center-line": 0.6411577363170983}, "ep001": {"driven_any": 9.430471007975555, "driven_lanedir": 9.265997053864972, "in-drivable-lane": 0, "deviation-heading": 1.0904146721043873, "deviation-center-line": 0.3223633780099237}, "ep002": {"driven_any": 0.29988041084327915, "driven_lanedir": 0.2684917204056503, "in-drivable-lane": 0, "deviation-heading": 0.13520363354596854, "deviation-center-line": 0.015365374787591144}, "ep003": {"driven_any": 18.668388349256983, "driven_lanedir": 18.396904358210985, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 1.8709080152054935, "deviation-center-line": 0.7871744447129276}, "ep004": {"driven_any": 18.696776827343896, "driven_lanedir": 18.375570987659035, "in-drivable-lane": 0.09999999999999976, "deviation-heading": 1.7021940189909166, "deviation-center-line": 0.7515808975245565}}
148151630yun chenPyTorch DDPG templateaido1_LF1_r3-v3step3-videossuccessyes3740:02:00(hidden)
other stats
videos1
148141630yun chenPyTorch DDPG templateaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median16.666666666666654


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": -0.4342557175937109, "good_angle": 13.661443780185737, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.933333333333325}, "ep001": {"nsteps": 500, "reward": -0.9751534127130872, "good_angle": 13.69979337689737, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.933333333333325}, "ep002": {"nsteps": 500, "reward": -0.8767128234133125, "good_angle": 13.68580815728423, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.933333333333325}, "ep003": {"nsteps": 500, "reward": -1.031129214178538, "good_angle": 13.695430305493003, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.89999999999999}, "ep004": {"nsteps": 500, "reward": -1.1838142353608272, "good_angle": 13.688732896242293, "survival_time": 16.666666666666654, "traveled_tiles": 1, "valid_direction": 12.966666666666656}}
good_angle_max13.69979337689737
good_angle_mean13.686241703220526
good_angle_median13.688732896242293
good_angle_min13.661443780185737
reward_max-0.4342557175937109
reward_mean-0.9002130806518952
reward_median-0.9751534127130872
reward_min-1.1838142353608272
survival_time_max16.666666666666654
survival_time_mean16.666666666666654
survival_time_min16.666666666666654
traveled_tiles_max1
traveled_tiles_mean1
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max12.966666666666656
valid_direction_mean12.933333333333325
valid_direction_median12.933333333333325
valid_direction_min12.89999999999999
148121628yun chenPyTorch DDPG templateaido1_LF1_r3-v3step1-simulationfailedyes3740:00:37
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 104, in run
    solve(params, cis)
  File "solution.py", line 55, in solve
    model.load("model", "models")
  File "/workspace/model.py", line 229, in load
    self.actor.load_state_dict(torch.load('{}/{}_actor.pth'.format(directory, filename), map_location=device))
  File "/opt/conda/lib/python2.7/site-packages/torch/nn/modules/module.py", line 719, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for ActorCNN:
	size mismatch for lin1.weight: copying a param of torch.Size([512, 4032]) from checkpoint, where the shape is torch.Size([512, 1792]) in current model.


(hidden)
148041619yun chenPyTorch DDPG templateaido1_LF1_r3-v3step3-videossuccessyes3740:02:02(hidden)
other stats
videos1
148001621JoelGaechtergreedy but not so lazyaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:16(hidden)
service_quality-20.769010795078295


other stats
efficiency-38.59020318031229
fleet_size-1000000000
147911625alezananaive with time correctionaido1_amod_efficiency_r1-v3step1-simulationsuccessyes3740:03:48(hidden)
other stats
passedtrue
147771610alezanarebalance testaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:04:18
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 68, in run
    command = dispatchingLogic.of(status)
  File "<decorator-gen-680>", line 2, in of
  File "/usr/local/lib/python3.5/dist-packages/contracts/main.py", line 243, in contracts_checker
    return function_(*args, **kwargs)
  File "/project/src/dispatcher/DispatcherHungarian.py", line 66, in of
    rebalance = self.rebalancer.of(roboTaxiToRebalance)
  File "<decorator-gen-679>", line 2, in of
  File "/usr/local/lib/python3.5/dist-packages/contracts/main.py", line 243, in contracts_checker
    return function_(*args, **kwargs)
  File "/project/src/rebalancers/RebalancerReqHeatmap.py", line 49, in of
    self.vectorFieldHeatmap = np.gradient(self.blurredReqHeatmap - self.blurredTaxiHeatmap)
AttributeError: 'RebalancerReqHeatmap' object has no attribute 'blurredTaxiHeatmap'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 68, in run
    command = dispatchingLogic.of(status)
  File "<decorator-gen-680>", line 2, in of
  File "/usr/local/lib/python3.5/dist-packages/contracts/main.py", line 243, in contracts_checker
    return function_(*args, **kwargs)
  File "/project/src/dispatcher/DispatcherHungarian.py", line 66, in of
    rebalance = self.rebalancer.of(roboTaxiToRebalance)
  File "<decorator-gen-679>", line 2, in of
  File "/usr/local/lib/python3.5/dist-packages/contracts/main.py", line 243, in contracts_checker
    return function_(*args, **kwargs)
  File "/project/src/rebalancers/RebalancerReqHeatmap.py", line 49, in of
    self.vectorFieldHeatmap = np.gradient(self.blurredReqHeatmap - self.blurredTaxiHeatmap)
AttributeError: 'RebalancerReqHeatmap' object has no attribute 'blurredTaxiHeatmap'


(hidden)
147661606Dzenan LapandicPanos grayscale inv_kine manualaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median3.3999999999999977


other stats
episodes
details{"ep000": {"nsteps": 106, "reward": -11.152171365213844, "good_angle": 5.489414207285546, "survival_time": 3.5333333333333306, "traveled_tiles": 2, "valid_direction": 1.499999999999995}, "ep001": {"nsteps": 118, "reward": -8.093575725110911, "good_angle": 0.30914087145253466, "survival_time": 3.933333333333329, "traveled_tiles": 3, "valid_direction": 0.466666666666665}, "ep002": {"nsteps": 72, "reward": -13.863376757026549, "good_angle": 0.8926388290068946, "survival_time": 2.4000000000000012, "traveled_tiles": 2, "valid_direction": 0.6999999999999993}, "ep003": {"nsteps": 99, "reward": -9.836605085850213, "good_angle": 0.49271255916801027, "survival_time": 3.299999999999998, "traveled_tiles": 2, "valid_direction": 0.5333333333333314}, "ep004": {"nsteps": 102, "reward": -9.58205123393632, "good_angle": 0.5281637785026528, "survival_time": 3.3999999999999977, "traveled_tiles": 3, "valid_direction": 0.5666666666666647}}
good_angle_max5.489414207285546
good_angle_mean1.5424140490831275
good_angle_median0.5281637785026528
good_angle_min0.30914087145253466
reward_max-8.093575725110911
reward_mean-10.505556033427563
reward_median-9.836605085850213
reward_min-13.863376757026549
survival_time_max3.933333333333329
survival_time_mean3.3133333333333312
survival_time_min2.4000000000000012
traveled_tiles_max3
traveled_tiles_mean2.4
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max1.499999999999995
valid_direction_mean0.753333333333331
valid_direction_median0.5666666666666647
valid_direction_min0.466666666666665
147601605Dzenan LapandicPanos grayscale inv_kine manualaido1_LFV_r1-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median2.4333333333333345


other stats
episodes
details{"ep000": {"nsteps": 213, "reward": -6.01127328629225, "good_angle": 14.737443424215051, "survival_time": 7.0999999999999845, "traveled_tiles": 5, "valid_direction": 1.5999999999999943}, "ep001": {"nsteps": 69, "reward": -13.988605902462766, "good_angle": 0.590424025078907, "survival_time": 2.3000000000000016, "traveled_tiles": 3, "valid_direction": 0.33333333333333215}, "ep002": {"nsteps": 73, "reward": -13.49780798010442, "good_angle": 0.5770462699162145, "survival_time": 2.4333333333333345, "traveled_tiles": 2, "valid_direction": 0.5666666666666653}, "ep003": {"nsteps": 108, "reward": -8.734442881170523, "good_angle": 0.3239660217639069, "survival_time": 3.599999999999997, "traveled_tiles": 4, "valid_direction": 0.33333333333333215}, "ep004": {"nsteps": 72, "reward": -13.461481005128007, "good_angle": 0.5872709793482211, "survival_time": 2.4000000000000012, "traveled_tiles": 3, "valid_direction": 0.33333333333333215}}
good_angle_max14.737443424215051
good_angle_mean3.363230144064461
good_angle_median0.5872709793482211
good_angle_min0.3239660217639069
reward_max-6.01127328629225
reward_mean-11.138722211031594
reward_median-13.461481005128007
reward_min-13.988605902462766
survival_time_max7.0999999999999845
survival_time_mean3.566666666666664
survival_time_min2.3000000000000016
traveled_tiles_max5
traveled_tiles_mean3.4
traveled_tiles_median3
traveled_tiles_min2
valid_direction_max1.5999999999999943
valid_direction_mean0.6333333333333313
valid_direction_median0.33333333333333215
valid_direction_min0.33333333333333215
147541604dcharrezTensorflowaido1_LF1_r3-v3step3-videossuccessyes3740:00:41(hidden)
other stats
videos1
147441599JoelGaechtergreedy but lazyaido1_amod_efficiency_r1-v3step1-simulationsuccessyes3740:03:17(hidden)
other stats
passedtrue
147201590WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationfailedyes3740:04:18
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 80, in run
    solve(gym_environment, cis)  # let's try to solve the challenge, exciting ah?
  File "solution.py", line 52, in solve
    observation, reward, done, info = env.step(action)
  File "/usr/local/lib/python2.7/dist-packages/gym/wrappers/time_limit.py", line 31, in step
    observation, reward, done, info = self.env.step(action)
  File "/notebooks/src/gym-duckietown-agent/gym_duckietown_agent/envs/simplesimagent_env.py", line 109, in step
    obs, rew, done, misc = self.sim.step(action, with_observation=True)
  File "/notebooks/src/duckietown-slimremote/duckietown_slimremote/pc/robot.py", line 55, in step
    return self._failsafe_observe(msg)
  File "/notebooks/src/duckietown-slimremote/duckietown_slimremote/pc/robot.py", line 86, in _failsafe_observe
    raise Exception(msg)
Exception: Giving up to connect to the gym duckietown server at host: evaluator


(hidden)
147101586JoelGaechtergreedy but lazyaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:03:24(hidden)
other stats
passedtrue
147011579heyt0nySAIC MOSCOW MMLaido1_LF1_r3-v3step4-vizsuccessyes3740:06:22(hidden)
driven_lanedir_median18.374895240961237
deviation-center-line_median0.8656538419910326
in-drivable-lane_median0.23333333333333428


other stats
deviation-center-line_max0.9935645070546248
deviation-center-line_mean0.8745118539030994
deviation-center-line_min0.7041326167548385
deviation-heading_max1.953749266474991
deviation-heading_mean1.6543168561776351
deviation-heading_median1.8473896759272523
deviation-heading_min1.1006422425282965
driven_any_max19.121711557000506
driven_any_mean18.872155478546425
driven_any_median18.95818703977651
driven_any_min18.611721358305232
driven_lanedir_max18.562286523319766
driven_lanedir_mean18.31830246027658
driven_lanedir_min17.99709990176779
in-drivable-lane_max0.5666666666666664
in-drivable-lane_mean0.3199999999999997
in-drivable-lane_min0.13333333333333297
per-episodes
details{"ep000": {"driven_any": 18.611721358305232, "driven_lanedir": 17.99709990176779, "in-drivable-lane": 0.5666666666666664, "deviation-heading": 1.1006422425282965, "deviation-center-line": 0.7041326167548385}, "ep001": {"driven_any": 19.121711557000506, "driven_lanedir": 18.374895240961237, "in-drivable-lane": 0.46666666666666534, "deviation-heading": 1.5081613516747892, "deviation-center-line": 0.9935645070546248}, "ep002": {"driven_any": 19.042816409745885, "driven_lanedir": 18.541100422672407, "in-drivable-lane": 0.1999999999999994, "deviation-heading": 1.8473896759272523, "deviation-center-line": 0.8492554751321502}, "ep003": {"driven_any": 18.626341027904004, "driven_lanedir": 18.1161302126617, "in-drivable-lane": 0.23333333333333428, "deviation-heading": 1.953749266474991, "deviation-center-line": 0.8656538419910326}, "ep004": {"driven_any": 18.95818703977651, "driven_lanedir": 18.562286523319766, "in-drivable-lane": 0.13333333333333297, "deviation-heading": 1.8616417442828468, "deviation-center-line": 0.9599528285828504}}
147001579heyt0nySAIC MOSCOW MMLaido1_LF1_r3-v3step3-videossuccessyes3740:02:05(hidden)
other stats
videos1
146891579heyt0nySAIC MOSCOW MMLaido1_LF1_r3-v3step1-simulationsuccessyes3740:05:20(hidden)
other stats
simulation-passed1
146881581JoelGaechterlazyaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:17(hidden)
service_quality-30.489251895284983


other stats
efficiency-58.13296758113725
fleet_size-1000000000
146851581JoelGaechterlazyaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:03:22(hidden)
other stats
passedtrue
146601567JoelGaechtertest submissionaido1_amod_service_quality_r1-v3step1-simulationerroryes3740:30:36
Timeout: Waited 180 [...]
Timeout:

Waited 1803.84992695 for container to finish. Giving up. 
(hidden)
146561564JoelGaechtertest submissionaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:03:28(hidden)
other stats
passedtrue
146491557JoelGaechterimproved defaultaido1_amod_service_quality_r1-v3step1-simulationerroryes3740:30:35
Timeout: Waited 180 [...]
Timeout:

Waited 1800.16386604 for container to finish. Giving up. 
(hidden)
146351550JoelGaechtertests submissionaido1_amod_service_quality_r1-v3step1-simulationerroryes3740:30:35
Timeout: Waited 180 [...]
Timeout:

Waited 1800.131387 for container to finish. Giving up. 
(hidden)
146311549miksazJetBrains Researchaido1_LF1_r3-v3step3-videossuccessyes3740:02:27(hidden)
other stats
videos1
146071539JoelGaechtertests submissionaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:13
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 46, in of
    roboTaxis = list(RoboTaxi(taxi) for taxi in status[1])
  File "/project/src/DispatchingLogic.py", line 46, in <genexpr>
    roboTaxis = list(RoboTaxi(taxi) for taxi in status[1])
  File "/project/src/utils/Agents.py", line 34, in __init__
    super(RoboTaxi, self).__init__(id, point)
TypeError: super() argument 1 must be type, not classobj


(hidden)
146011534zgxsinRandom executionaido1_LF1_r3-v3step3-videossuccessyes3740:00:36(hidden)
other stats
videos1
145991534zgxsinRandom executionaido1_LF1_r3-v3step1-simulationsuccessyes3740:01:17(hidden)
other stats
simulation-passed1
145971532JoelGaechtertests submissionaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:01
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 48, in of
    e.args += '\n' + '\n'.join(status)
TypeError: sequence item 0: expected string, int found


(hidden)
145951530JoelGaechtertests submissionaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:04
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 49, in of
    roboTaxis = list(RoboTaxi(taxi) for taxi in status[1])
  File "/project/src/DispatchingLogic.py", line 49, in <genexpr>
    roboTaxis = list(RoboTaxi(taxi) for taxi in status[1])
  File "/project/src/utils/Agents.py", line 30, in __init__
    assert isinstance(is_divertable, bool), "Expected <bool>, got <%s>: %s!" % (type(is_divertable), is_divertable)
AssertionError: Expected <bool>, got <<type 'int'>>: 1!


(hidden)
145941529JoelGaechtertests submissionaido1_amod_service_quality_r1-v3step2-scoringsuccessyes3740:00:18(hidden)
service_quality-33.09704247520126


other stats
efficiency-66.99472990080554
fleet_size-1000000000
145931529JoelGaechtertests submissionaido1_amod_service_quality_r1-v3step1-simulationsuccessyes3740:02:40(hidden)
other stats
passedtrue
145921528JoelGaechtertest submissionaido1_amod_service_quality_r1-v3step1-simulationfailedyes3740:03:10
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/amod/target/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "/project/solution.py", line 13, in run
    aidoGuest.run()
  File "/project/src/AidoGuest.py", line 62, in run
    command = dispatchingLogic.of(status)
  File "/project/src/DispatchingLogic.py", line 48, in of
    roboTaxis = list(RoboTaxi(taxi) for taxi in status[1])
  File "/project/src/DispatchingLogic.py", line 48, in <genexpr>
    roboTaxis = list(RoboTaxi(taxi) for taxi in status[1])
  File "/project/src/utils/Agents.py", line 30, in __init__
    assert isinstance(is_divertable, bool)
AssertionError


(hidden)
145911525trimcaoBaseline solution using imitation learning from logsaido1_LF1_r3-v3step4-vizsuccessyes3740:01:35(hidden)
driven_lanedir_median0.04154583737552131
deviation-center-line_median0.03827880017070424
in-drivable-lane_median0.16666666666666669


other stats
deviation-center-line_max1.079144640396308
deviation-center-line_mean0.2451150738508613
deviation-center-line_min0.030603342612577404
deviation-heading_max5.59004758130579
deviation-heading_mean1.4155311302452172
deviation-heading_median0.3907776643645585
deviation-heading_min0.296834134820943
driven_any_max2.370108680473008
driven_any_mean0.5397436880261288
driven_any_median0.08562021228978281
driven_any_min0.05721557929262899
driven_lanedir_max0.34482585890770956
driven_lanedir_mean0.10194960578175533
driven_lanedir_min0.038399165505968114
in-drivable-lane_max11.400000000000002
in-drivable-lane_mean2.386666666666667
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 2.370108680473008, "driven_lanedir": 0.34482585890770956, "in-drivable-lane": 11.400000000000002, "deviation-heading": 5.59004758130579, "deviation-center-line": 1.079144640396308}, "ep001": {"driven_any": 0.08562021228978281, "driven_lanedir": 0.038399165505968114, "in-drivable-lane": 0.16666666666666669, "deviation-heading": 0.3907776643645585, "deviation-center-line": 0.03352723306971059}, "ep002": {"driven_any": 0.11427823279261956, "driven_lanedir": 0.04484941489094396, "in-drivable-lane": 0.29999999999999993, "deviation-heading": 0.4246923720178235, "deviation-center-line": 0.030603342612577404}, "ep003": {"driven_any": 0.07149573528260494, "driven_lanedir": 0.04012775222863363, "in-drivable-lane": 0.06666666666666671, "deviation-heading": 0.3753038987169689, "deviation-center-line": 0.03827880017070424}, "ep004": {"driven_any": 0.05721557929262899, "driven_lanedir": 0.04154583737552131, "in-drivable-lane": 0, "deviation-heading": 0.296834134820943, "deviation-center-line": 0.04402135300500615}}
145841523ngkelPyTorch DDPG templateaido1_LF1_r3-v3step1-simulationerroryes3740:00:33
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
145791521trimcaoTensorflow templateaido1_LF1_r3-v3step3-videossuccessyes3740:01:18(hidden)
other stats
videos1
145631508trimcaoBaseline solution using imitation learning from logsaido1_LF1_r3-v3step4-vizsuccessyes3740:01:21(hidden)
driven_lanedir_median0.7301024697144504
deviation-center-line_median0.1574295604398948
in-drivable-lane_median0


other stats
deviation-center-line_max0.2585764977657999
deviation-center-line_mean0.14148118868396894
deviation-center-line_min0.017980234847199635
deviation-heading_max0.5254370924565497
deviation-heading_mean0.3404962885820816
deviation-heading_median0.3692819821072087
deviation-heading_min0.13124725671984125
driven_any_max1.1530398154811368
driven_any_mean0.8968087450912916
driven_any_median0.8152806776253959
driven_any_min0.6405776748808817
driven_lanedir_max1.11733610765508
driven_lanedir_mean0.6600144161154979
driven_lanedir_min0.00028817719919516094
in-drivable-lane_max2.833333333333333
in-drivable-lane_mean0.5733333333333333
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.0948054805194454, "driven_lanedir": 0.00028817719919516094, "in-drivable-lane": 2.833333333333333, "deviation-heading": 0.5226688772800787, "deviation-center-line": 0.017980234847199635}, "ep001": {"driven_any": 0.6405776748808817, "driven_lanedir": 0.6383744506051681, "in-drivable-lane": 0, "deviation-heading": 0.15384623434672984, "deviation-center-line": 0.1574295604398948}, "ep002": {"driven_any": 0.780340076949599, "driven_lanedir": 0.7301024697144504, "in-drivable-lane": 0, "deviation-heading": 0.5254370924565497, "deviation-center-line": 0.06646140648602601}, "ep003": {"driven_any": 0.8152806776253959, "driven_lanedir": 0.8139708754035957, "in-drivable-lane": 0, "deviation-heading": 0.13124725671984125, "deviation-center-line": 0.20695824388092443}, "ep004": {"driven_any": 1.1530398154811368, "driven_lanedir": 1.11733610765508, "in-drivable-lane": 0.033333333333333215, "deviation-heading": 0.3692819821072087, "deviation-center-line": 0.2585764977657999}}
145601515trimcaoTensorflow templateaido1_LF1_r3-v3step3-videossuccessyes3740:01:10(hidden)
other stats
videos1
145491510trimcaoBaseline solution using imitation learning from logsaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:20(hidden)
survival_time_median0.7


other stats
episodes
details{"ep000": {"nsteps": 52, "reward": -19.694612874380816, "good_angle": 1.383743437929927, "survival_time": 1.7333333333333354, "traveled_tiles": 1, "valid_direction": 1.300000000000002}, "ep001": {"nsteps": 21, "reward": -47.42587910654644, "good_angle": 0.1468851811926694, "survival_time": 0.7, "traveled_tiles": 2, "valid_direction": 0.4333333333333333}, "ep002": {"nsteps": 26, "reward": -38.13889562367247, "good_angle": 0.16618264895054685, "survival_time": 0.8666666666666666, "traveled_tiles": 1, "valid_direction": 0.4666666666666666}, "ep003": {"nsteps": 21, "reward": -47.45366268463078, "good_angle": 0.13115758897935711, "survival_time": 0.7, "traveled_tiles": 1, "valid_direction": 0.4}, "ep004": {"nsteps": 19, "reward": -52.47200886894164, "good_angle": 0.07661285097352322, "survival_time": 0.6333333333333333, "traveled_tiles": 2, "valid_direction": 0.26666666666666666}}
good_angle_max1.383743437929927
good_angle_mean0.3809163416052047
good_angle_median0.1468851811926694
good_angle_min0.07661285097352322
reward_max-19.694612874380816
reward_mean-41.03701183163442
reward_median-47.42587910654644
reward_min-52.47200886894164
survival_time_max1.7333333333333354
survival_time_mean0.926666666666667
survival_time_min0.6333333333333333
traveled_tiles_max2
traveled_tiles_mean1.4
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max1.300000000000002
valid_direction_mean0.5733333333333337
valid_direction_median0.4333333333333333
valid_direction_min0.26666666666666666
145361502WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationerroryes3740:00:33
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
145341501WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationerroryes3740:00:32
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
145331500YIPTSZFUNGSolution templateaido1_luck-v3step1successyes3740:00:22(hidden)
score183.98809481020888


145291499miksazJetBrains Researchaido1_LF1_r3-v3step1-simulationsuccessyes3740:09:41(hidden)
other stats
simulation-passed1
145281498WEIGAOFirst trialaido1_LF1_r3-v3step4-vizsuccessyes3740:01:20(hidden)
driven_lanedir_median0.7301206548774075
deviation-center-line_median0.1446230783264199
in-drivable-lane_median0.10000000000000007


other stats
deviation-center-line_max0.23080438474521345
deviation-center-line_mean0.1374847744356198
deviation-center-line_min0.03864452894642544
deviation-heading_max1.1463392208025016
deviation-heading_mean0.6733004323959972
deviation-heading_median0.5964209438590051
deviation-heading_min0.11829174291972248
driven_any_max1.3586678772168903
driven_any_mean1.0161238757945594
driven_any_median1.1071907524478712
driven_any_min0.649625241203225
driven_lanedir_max1.047653673512643
driven_lanedir_mean0.6889685962878327
driven_lanedir_min0.0332079634330551
in-drivable-lane_max2.200000000000002
in-drivable-lane_mean0.5200000000000002
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.1071907524478712, "driven_lanedir": 0.0332079634330551, "in-drivable-lane": 2.200000000000002, "deviation-heading": 0.5964209438590051, "deviation-center-line": 0.03864452894642544}, "ep001": {"driven_any": 1.3586678772168903, "driven_lanedir": 1.047653673512643, "in-drivable-lane": 0.29999999999999893, "deviation-heading": 1.1463392208025016, "deviation-center-line": 0.23080438474521345}, "ep002": {"driven_any": 0.7851160894904319, "driven_lanedir": 0.7301206548774075, "in-drivable-lane": 0, "deviation-heading": 0.4874217582725851, "deviation-center-line": 0.06023847910511712}, "ep003": {"driven_any": 0.649625241203225, "driven_lanedir": 0.6474777508088774, "in-drivable-lane": 0, "deviation-heading": 0.11829174291972248, "deviation-center-line": 0.1446230783264199}, "ep004": {"driven_any": 1.1800194186143782, "driven_lanedir": 0.9863829388071804, "in-drivable-lane": 0.10000000000000007, "deviation-heading": 1.018028496126172, "deviation-center-line": 0.2131134010549231}}
145271498WEIGAOFirst trialaido1_LF1_r3-v3step3-videossuccessyes3740:00:42(hidden)
other stats
videos1
145261498WEIGAOFirst trialaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median2.666666666666667


other stats
episodes
details{"ep000": {"nsteps": 80, "reward": -13.469280205247925, "good_angle": 1.3227228326058795, "survival_time": 2.666666666666667, "traveled_tiles": 2, "valid_direction": 0.9999999999999984}, "ep001": {"nsteps": 99, "reward": -9.458201678996586, "good_angle": 0.5245551182650602, "survival_time": 3.299999999999998, "traveled_tiles": 3, "valid_direction": 0.7333333333333307}, "ep002": {"nsteps": 58, "reward": -16.806177047948385, "good_angle": 0.3261183224622542, "survival_time": 1.933333333333336, "traveled_tiles": 2, "valid_direction": 0.6666666666666687}, "ep003": {"nsteps": 48, "reward": -20.51388234932286, "good_angle": 0.01298754709182761, "survival_time": 1.6000000000000016, "traveled_tiles": 2, "valid_direction": 0}, "ep004": {"nsteps": 86, "reward": -11.002000446480071, "good_angle": 0.48928289529392543, "survival_time": 2.8666666666666663, "traveled_tiles": 3, "valid_direction": 0.7333333333333307}}
good_angle_max1.3227228326058795
good_angle_mean0.5351333431437895
good_angle_median0.48928289529392543
good_angle_min0.01298754709182761
reward_max-9.458201678996586
reward_mean-14.249908345599165
reward_median-13.469280205247925
reward_min-20.51388234932286
survival_time_max3.299999999999998
survival_time_mean2.473333333333334
survival_time_min1.6000000000000016
traveled_tiles_max3
traveled_tiles_mean2.4
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0.9999999999999984
valid_direction_mean0.6266666666666657
valid_direction_median0.7333333333333307
valid_direction_min0
145251498WEIGAOFirst trialaido1_LF1_r3-v3step1-simulationsuccessyes3740:01:26(hidden)
other stats
simulation-passed1
145181495yangzm11Tensorflow templateaido1_LF1_r3-v3step4-vizsuccessyes3740:03:28(hidden)
driven_lanedir_median5.302566481656299
deviation-center-line_median0.5565671916823862
in-drivable-lane_median0.16666666666666652


other stats
deviation-center-line_max0.8859936557711611
deviation-center-line_mean0.528607812747053
deviation-center-line_min0.16606102536010822
deviation-heading_max3.033370854841319
deviation-heading_mean2.280705656084282
deviation-heading_median2.4561990578057573
deviation-heading_min0.9882049312556496
driven_any_max9.0481182555653
driven_any_mean6.141090110593249
driven_any_median5.90889969385339
driven_any_min1.2967673217013216
driven_lanedir_max8.21557018025004
driven_lanedir_mean5.398798112513221
driven_lanedir_min1.1127930583588117
in-drivable-lane_max1.433333333333331
in-drivable-lane_mean0.5466666666666657
in-drivable-lane_min0.03333333333333344
per-episodes
details{"ep000": {"driven_any": 5.90889969385339, "driven_lanedir": 4.810083815920299, "in-drivable-lane": 0.9666666666666656, "deviation-heading": 2.4510219442237524, "deviation-center-line": 0.44833422712128373}, "ep001": {"driven_any": 9.0481182555653, "driven_lanedir": 7.552977026380654, "in-drivable-lane": 1.433333333333331, "deviation-heading": 2.4747314922949313, "deviation-center-line": 0.5565671916823862}, "ep002": {"driven_any": 8.688836269603568, "driven_lanedir": 8.21557018025004, "in-drivable-lane": 0.13333333333333308, "deviation-heading": 3.033370854841319, "deviation-center-line": 0.8859936557711611}, "ep003": {"driven_any": 5.762829012242665, "driven_lanedir": 5.302566481656299, "in-drivable-lane": 0.16666666666666652, "deviation-heading": 2.4561990578057573, "deviation-center-line": 0.5860829638003254}, "ep004": {"driven_any": 1.2967673217013216, "driven_lanedir": 1.1127930583588117, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.9882049312556496, "deviation-center-line": 0.16606102536010822}}
145101494miksazJetBrains Researchaido1_LF1_r3-v3step1-simulationsuccessyes3740:05:38(hidden)
other stats
simulation-passed1
145021490yangzm11Random executionaido1_LF1_r3-v3step4-vizsuccessyes3740:01:25(hidden)
driven_lanedir_median0.951413325807142
deviation-center-line_median0.16102726552119723
in-drivable-lane_median0.06666666666666665


other stats
deviation-center-line_max0.17820149281585146
deviation-center-line_mean0.128105946363745
deviation-center-line_min0.06211537039671858
deviation-heading_max0.7560517968410126
deviation-heading_mean0.5097925946113284
deviation-heading_median0.5108171830251238
deviation-heading_min0.17157452535908832
driven_any_max1.3300707264529543
driven_any_mean1.0809740518732094
driven_any_median1.160446784679361
driven_any_min0.7853792046207235
driven_lanedir_max1.2856569817914771
driven_lanedir_mean0.8183889801198043
driven_lanedir_min0.11606379077114992
in-drivable-lane_max2.200000000000002
in-drivable-lane_mean0.46666666666666706
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.1736126052178162, "driven_lanedir": 0.11606379077114992, "in-drivable-lane": 2.200000000000002, "deviation-heading": 0.6922450337004293, "deviation-center-line": 0.06211537039671858}, "ep001": {"driven_any": 1.3300707264529543, "driven_lanedir": 1.2856569817914771, "in-drivable-lane": 0, "deviation-heading": 0.4182744341309882, "deviation-center-line": 0.17820149281585146}, "ep002": {"driven_any": 0.7853792046207235, "driven_lanedir": 0.6971297913199096, "in-drivable-lane": 0.06666666666666665, "deviation-heading": 0.5108171830251238, "deviation-center-line": 0.06963789459773485}, "ep003": {"driven_any": 0.9553609383951917, "driven_lanedir": 0.951413325807142, "in-drivable-lane": 0, "deviation-heading": 0.17157452535908832, "deviation-center-line": 0.16954770848722292}, "ep004": {"driven_any": 1.160446784679361, "driven_lanedir": 1.0416810109093426, "in-drivable-lane": 0.06666666666666687, "deviation-heading": 0.7560517968410126, "deviation-center-line": 0.16102726552119723}}
144751479dcharrezTensorflowaido1_LF1_r3-v3step4-vizsuccessyes3740:02:18(hidden)
driven_lanedir_median3.912410463621699
deviation-center-line_median0.3394407796069116
in-drivable-lane_median0.7666666666666646


other stats
deviation-center-line_max0.5690578088985517
deviation-center-line_mean0.3542525361680949
deviation-center-line_min0.07378488425042747
deviation-heading_max2.2498074438650657
deviation-heading_mean1.3228314192291948
deviation-heading_median1.3235423963544786
deviation-heading_min0.4378611918536576
driven_any_max9.16565724747236
driven_any_mean5.634502703007512
driven_any_median5.238123019641708
driven_any_min1.1796020363280564
driven_lanedir_max7.885877247151207
driven_lanedir_mean4.660854284539443
driven_lanedir_min0.8843714505344514
in-drivable-lane_max1.2999999999999985
in-drivable-lane_mean0.6199999999999988
in-drivable-lane_min0.13333333333333308
per-episodes
details{"ep000": {"driven_any": 5.238123019641708, "driven_lanedir": 3.511712058079899, "in-drivable-lane": 1.2999999999999985, "deviation-heading": 1.3235423963544786, "deviation-center-line": 0.3394407796069116}, "ep001": {"driven_any": 9.16565724747236, "driven_lanedir": 7.885877247151207, "in-drivable-lane": 0.7666666666666646, "deviation-heading": 2.2498074438650657, "deviation-center-line": 0.5690578088985517}, "ep002": {"driven_any": 8.320350077927046, "driven_lanedir": 7.109900203309957, "in-drivable-lane": 0.766666666666665, "deviation-heading": 1.7295840664968274, "deviation-center-line": 0.478976198736539}, "ep003": {"driven_any": 4.26878113366839, "driven_lanedir": 3.912410463621699, "in-drivable-lane": 0.13333333333333308, "deviation-heading": 0.8733619975759445, "deviation-center-line": 0.3100030093480445}, "ep004": {"driven_any": 1.1796020363280564, "driven_lanedir": 0.8843714505344514, "in-drivable-lane": 0.1333333333333333, "deviation-heading": 0.4378611918536576, "deviation-center-line": 0.07378488425042747}}
144691478dcharrezTensorflowaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:25(hidden)
survival_time_median0.8999999999999999


other stats
episodes
details{"ep000": {"nsteps": 20, "reward": -50.12374418806285, "good_angle": 0.4219849326587856, "survival_time": 0.6666666666666666, "traveled_tiles": 1, "valid_direction": 0.6333333333333333}, "ep001": {"nsteps": 46, "reward": -21.2822488821719, "good_angle": 0.5678469021278189, "survival_time": 1.5333333333333348, "traveled_tiles": 3, "valid_direction": 0.4000000000000013}, "ep002": {"nsteps": 27, "reward": -36.76631972193718, "good_angle": 0.5305129009986533, "survival_time": 0.8999999999999999, "traveled_tiles": 2, "valid_direction": 0.4}, "ep003": {"nsteps": 36, "reward": -26.99155345124503, "good_angle": 0.2503323129869034, "survival_time": 1.2000000000000004, "traveled_tiles": 2, "valid_direction": 0.26666666666666716}, "ep004": {"nsteps": 25, "reward": -39.389293301105496, "good_angle": 0.3114218110823823, "survival_time": 0.8333333333333333, "traveled_tiles": 1, "valid_direction": 0.19999999999999996}}
good_angle_max0.5678469021278189
good_angle_mean0.4164197719709087
good_angle_median0.4219849326587856
good_angle_min0.2503323129869034
reward_max-21.2822488821719
reward_mean-34.910631908904485
reward_median-36.76631972193718
reward_min-50.12374418806285
survival_time_max1.5333333333333348
survival_time_mean1.0266666666666668
survival_time_min0.6666666666666666
traveled_tiles_max3
traveled_tiles_mean1.8
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max0.6333333333333333
valid_direction_mean0.3800000000000003
valid_direction_median0.4
valid_direction_min0.19999999999999996
144681478dcharrezTensorflowaido1_LF1_r3-v3step1-simulationsuccessyes3740:01:12(hidden)
other stats
simulation-passed1
144631476dcharrezTensorflowaido1_LF1_r3-v3step1-simulationfailedyes3740:00:35
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 86, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 33, in solve
    graph_location='tf_models/')  # this is the folder where our models are stored.
  File "/workspace/model.py", line 60, in __init__
    self._initialize(observation_shape, action_shape, graph_location)
  File "/workspace/model.py", line 84, in _initialize
    self._storing(storage_location)
  File "/workspace/model.py", line 103, in _storing
    self.tf_saver.restore(self.tf_session, self.tf_checkpoint)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1546, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
DataLossError: Unable to open table file /workspace/tf_models: Failed precondition: /workspace/tf_models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
	 [[node save/RestoreV2 (defined at /workspace/model.py:99)  = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

Caused by op u'save/RestoreV2', defined at:
  File "solution.py", line 94, in <module>
    wrap_solution(Submission())
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 86, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 33, in solve
    graph_location='tf_models/')  # this is the folder where our models are stored.
  File "/workspace/model.py", line 60, in __init__
    self._initialize(observation_shape, action_shape, graph_location)
  File "/workspace/model.py", line 84, in _initialize
    self._storing(storage_location)
  File "/workspace/model.py", line 99, in _storing
    self.tf_saver = tf.train.Saver()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1102, in __init__
    self.build()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1114, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1151, in _build
    build_save=build_save, build_restore=build_restore)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 795, in _build_internal
    restore_sequentially, reshape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 406, in _AddRestoreOps
    restore_sequentially)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 862, in bulk_restore
    return io_ops.restore_v2(filename_tensor, names, slices, dtypes)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1466, in restore_v2
    shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
    self._traceback = tf_stack.extract_stack()

DataLossError (see above for traceback): Unable to open table file /workspace/tf_models: Failed precondition: /workspace/tf_models; Is a directory: perhaps your file is in a different file format and you need to use a different restore operator?
	 [[node save/RestoreV2 (defined at /workspace/model.py:99)  = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]



(hidden)
144621475dcharrezTensorflowaido1_LF1_r3-v3step1-simulationfailedyes3740:00:37
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 86, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 33, in solve
    graph_location='tf_models/')  # this is the folder where our models are stored.
  File "/workspace/model.py", line 60, in __init__
    self._initialize(observation_shape, action_shape, graph_location)
  File "/workspace/model.py", line 84, in _initialize
    self._storing(storage_location)
  File "/workspace/model.py", line 101, in _storing
    self.tf_checkpoint = tf.train.latest_checkpoint(location)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/checkpoint_management.py", line 338, in latest_checkpoint
    if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/lib/io/file_io.py", line 344, in get_matching_files
    compat.as_bytes(single_filename), status)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
    c_api.TF_GetCode(self.status.status))
NotFoundError: /opt/ml/model; No such file or directory


(hidden)
144611474dcharrezTensorflowaido1_LF1_r3-v3step1-simulationfailedyes3740:00:42
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 86, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 33, in solve
    graph_location='tf_models/')  # this is the folder where our models are stored.
  File "/workspace/model.py", line 60, in __init__
    self._initialize(observation_shape, action_shape, graph_location)
  File "/workspace/model.py", line 84, in _initialize
    self._storing(storage_location)
  File "/workspace/model.py", line 101, in _storing
    self.tf_checkpoint = tf.train.latest_checkpoint(location)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/checkpoint_management.py", line 338, in latest_checkpoint
    if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/lib/io/file_io.py", line 344, in get_matching_files
    compat.as_bytes(single_filename), status)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
    c_api.TF_GetCode(self.status.status))
NotFoundError: /opt/ml/model; No such file or directory


(hidden)
144491462karlji1021PyTorch Rainbow by KarlJiaido1_LF1_r3-v3step1-simulationfailedyes3740:00:43
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 261, in run
    solve(params, cis)
  File "solution.py", line 203, in solve
    Variable(storage.current_obs()),
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
  File "solution.py", line 110, in <lambda>
    Variable = lambda *args, **kwargs: torch.autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else Variable(*args, **kwargs)
RuntimeError: maximum recursion depth exceeded


(hidden)
144451458karlji1021PyTorch Rainbow by KarlJiaido1_LF1_r3-v3step1-simulationfailedyes3740:00:41
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 252, in run
    solve(params, cis)
  File "solution.py", line 142, in solve
    current_obs = update_obs(obs)
  File "solution.py", line 137, in update_obs
    obs = obs.view(-1, 160, 100)
RuntimeError: invalid argument 2: size '[-1 x 160 x 100]' is invalid for input with 883200 elements at /tmp/pip-req-build-vRRdPa/aten/src/TH/THStorage.cpp:80


(hidden)
144291441karlji1021PyTorch Rainbow by KarlJiaido1_LF1_r3-v3step1-simulationfailedyes3740:00:27
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 239, in run
    solve(params, cis)
  File "solution.py", line 111, in solve
    env = gym.make('env')
  File "/opt/conda/lib/python2.7/site-packages/gym/envs/registration.py", line 167, in make
    return registry.make(id)
  File "/opt/conda/lib/python2.7/site-packages/gym/envs/registration.py", line 118, in make
    spec = self.spec(id)
  File "/opt/conda/lib/python2.7/site-packages/gym/envs/registration.py", line 140, in spec
    raise error.Error('Attempted to look up malformed environment ID: {}. (Currently all IDs must be of the form {}.)'.format(id.encode('utf-8'), env_id_re.pattern))
Error: Attempted to look up malformed environment ID: env. (Currently all IDs must be of the form ^(?:[\w:-]+\/)?([\w:.-]+)-v(\d+)$.)


(hidden)
144281442rumpelstilzchenMein erster Zauberspruchaido1_amod_fleet_size_r1-v3step2-scoringsuccessyes3740:00:18(hidden)
fleet_size-1000000000


other stats
efficiency-66.08538955196474
service_quality-33.01733738799115
144261442rumpelstilzchenMein erster Zauberspruchaido1_amod_fleet_size_r1-v3step1-simulationsuccessyes3740:03:39(hidden)
other stats
passedtrue
144201437karlji1021PyTorch Rainbow by KarlJiaido1_LF1_r3-v3step1-simulationerroryes3740:00:39
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
144081425rumpelstilzchenMein erster Zauberspruchaido1_amod_efficiency_r1-v3step1-simulationerroryes3740:30:46
Timeout: Waited 180 [...]
Timeout:

Waited 1803.68270206 for container to finish. Giving up. 
(hidden)
144061423GWLeePyTorch DDPG algorithm by Giwoong Leeaido1_LF1_r3-v3step1-simulationerroryes3740:01:12
InvalidEvaluator: Tr [...]
InvalidEvaluator:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 488, in wrap_evaluator
    evaluator.score(cie)
  File "eval.py", line 97, in score
    raise dc.InvalidEvaluator(msg)
InvalidEvaluator: Gym exited with code 2
(hidden)
144031420karlji1021PyTorch templateaido1_LF1_r3-v3step1-simulationerroryes3740:00:34
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
143891406karlji1021PyTorch templateaido1_LF1_r3-v3step1-simulationerroryes3740:00:34
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
143871403GWLeePyTorch DDPG algorithm by Giwoong Leeaido1_LF1_r3-v3step1-simulationerroryes3740:00:34
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
143821399GWLeePyTorch DDPG algorithm by Giwoong Leeaido1_LF1_r3-v3step1-simulationerroryes3740:00:47
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
143731395Liam PaullTemplate for ROS Submissionaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median1.466666666666668


other stats
episodes
details{"ep000": {"nsteps": 19, "reward": -54.082200448764, "good_angle": 0.29412592982952523, "survival_time": 0.6333333333333333, "traveled_tiles": 1, "valid_direction": 0.36666666666666664}, "ep001": {"nsteps": 57, "reward": -17.829838529605727, "good_angle": 1.0597289026684449, "survival_time": 1.9000000000000024, "traveled_tiles": 3, "valid_direction": 1.5666666666666689}, "ep002": {"nsteps": 65, "reward": -15.522587250860838, "good_angle": 1.3407229417322886, "survival_time": 2.1666666666666687, "traveled_tiles": 3, "valid_direction": 1.500000000000002}, "ep003": {"nsteps": 14, "reward": -71.17481030257684, "good_angle": 0.0861165807717722, "survival_time": 0.4666666666666666, "traveled_tiles": 1, "valid_direction": 0.19999999999999996}, "ep004": {"nsteps": 44, "reward": -23.28891708193855, "good_angle": 0.942620846710996, "survival_time": 1.466666666666668, "traveled_tiles": 2, "valid_direction": 1.0333333333333343}}
good_angle_max1.3407229417322886
good_angle_mean0.7446630403426052
good_angle_median0.942620846710996
good_angle_min0.0861165807717722
reward_max-15.522587250860838
reward_mean-36.37967072274919
reward_median-23.28891708193855
reward_min-71.17481030257684
survival_time_max2.1666666666666687
survival_time_mean1.3266666666666678
survival_time_min0.4666666666666666
traveled_tiles_max3
traveled_tiles_mean2
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max1.5666666666666689
valid_direction_mean0.9333333333333342
valid_direction_median1.0333333333333343
valid_direction_min0.19999999999999996
143711394tongqiusuiNvidia CNNaido1_LF1_r3-v3step4-vizsuccessyes3740:02:05(hidden)
driven_lanedir_median-0.02224635332038316
deviation-center-line_median0.005519627355163419
in-drivable-lane_median1.9666666666666688


other stats
deviation-center-line_max1.1549148130237308
deviation-center-line_mean0.3030731343684491
deviation-center-line_min0.0016557031709663343
deviation-heading_max2.463178719184934
deviation-heading_mean0.9492344435328436
deviation-heading_median0.08970785290292499
deviation-heading_min0.07838763421317246
driven_any_max7.685708752927946
driven_any_mean3.229157116789841
driven_any_median1.1712334450306658
driven_any_min0.9902330309498452
driven_lanedir_max0.8901639185850052
driven_lanedir_mean0.21578944966750377
driven_lanedir_min-0.02720112841212208
in-drivable-lane_max7.166666666666651
in-drivable-lane_mean3.879999999999995
in-drivable-lane_min1.7333333333333356
per-episodes
details{"ep000": {"driven_any": 7.685708752927946, "driven_lanedir": 0.8901639185850052, "in-drivable-lane": 6.733333333333318, "deviation-heading": 2.463178719184934, "deviation-center-line": 1.1549148130237308}, "ep001": {"driven_any": 5.206121927765607, "driven_lanedir": 0.26349148662626787, "in-drivable-lane": 7.166666666666651, "deviation-heading": 2.0319891231479708, "deviation-center-line": 0.3506737549627578}, "ep002": {"driven_any": 1.1712334450306658, "driven_lanedir": -0.02720112841212208, "in-drivable-lane": 1.9666666666666688, "deviation-heading": 0.07838763421317246, "deviation-center-line": 0.0016557031709663343}, "ep003": {"driven_any": 1.0924884272751414, "driven_lanedir": -0.02224635332038316, "in-drivable-lane": 1.8000000000000025, "deviation-heading": 0.08970785290292499, "deviation-center-line": 0.0026017733296273293}, "ep004": {"driven_any": 0.9902330309498452, "driven_lanedir": -0.02526067514124919, "in-drivable-lane": 1.7333333333333356, "deviation-heading": 0.08290888821521611, "deviation-center-line": 0.005519627355163419}}
143691394tongqiusuiNvidia CNNaido1_LF1_r3-v3step3-videossuccessyes3740:01:01(hidden)
other stats
videos1
143681394tongqiusuiNvidia CNNaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median2.1000000000000023


other stats
episodes
details{"ep000": {"nsteps": 444, "reward": -2.4158616550745906, "good_angle": 3.843994262822933, "survival_time": 14.799999999999958, "traveled_tiles": 4, "valid_direction": 7.69999999999998}, "ep001": {"nsteps": 299, "reward": -4.453532826266194, "good_angle": 8.539832989906227, "survival_time": 9.966666666666642, "traveled_tiles": 2, "valid_direction": 8.666666666666645}, "ep002": {"nsteps": 63, "reward": -17.929979336403665, "good_angle": 1.4323361903637182, "survival_time": 2.1000000000000023, "traveled_tiles": 1, "valid_direction": 1.8666666666666687}, "ep003": {"nsteps": 58, "reward": -19.342962585261155, "good_angle": 1.2874436730123031, "survival_time": 1.933333333333336, "traveled_tiles": 2, "valid_direction": 1.7666666666666688}, "ep004": {"nsteps": 56, "reward": -19.98277850156384, "good_angle": 1.0951512160167225, "survival_time": 1.8666666666666691, "traveled_tiles": 1, "valid_direction": 1.633333333333335}}
good_angle_max8.539832989906227
good_angle_mean3.239751666424381
good_angle_median1.4323361903637182
good_angle_min1.0951512160167225
reward_max-2.4158616550745906
reward_mean-12.82502298091389
reward_median-17.929979336403665
reward_min-19.98277850156384
survival_time_max14.799999999999958
survival_time_mean6.133333333333322
survival_time_min1.8666666666666691
traveled_tiles_max4
traveled_tiles_mean2
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max8.666666666666645
valid_direction_mean4.326666666666659
valid_direction_median1.8666666666666687
valid_direction_min1.633333333333335
143351381miksazRL solutionaido1_LF1_r3-v3step3-videossuccessyes3740:01:13(hidden)
other stats
videos1
143341381miksazRL solutionaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median2.333333333333335


other stats
episodes
details{"ep000": {"nsteps": 49, "reward": -20.36872922716548, "good_angle": 1.884751874914036, "survival_time": 1.633333333333335, "traveled_tiles": 3, "valid_direction": 1.133333333333335}, "ep001": {"nsteps": 282, "reward": -3.19939773795294, "good_angle": 2.7762206037646115, "survival_time": 9.399999999999975, "traveled_tiles": 14, "valid_direction": 0.966666666666664}, "ep002": {"nsteps": 305, "reward": -3.311788877405104, "good_angle": 8.146741007817505, "survival_time": 10.16666666666664, "traveled_tiles": 9, "valid_direction": 2.499999999999991}, "ep003": {"nsteps": 53, "reward": -18.543739185158937, "good_angle": 0.6783711266662208, "survival_time": 1.7666666666666688, "traveled_tiles": 3, "valid_direction": 0.6333333333333352}, "ep004": {"nsteps": 70, "reward": -14.21434847329344, "good_angle": 0.8611544510709545, "survival_time": 2.333333333333335, "traveled_tiles": 4, "valid_direction": 0.7333333333333332}}
good_angle_max8.146741007817505
good_angle_mean2.8694478128466656
good_angle_median1.884751874914036
good_angle_min0.6783711266662208
reward_max-3.19939773795294
reward_mean-11.92760070019518
reward_median-14.21434847329344
reward_min-20.36872922716548
survival_time_max10.16666666666664
survival_time_mean5.059999999999992
survival_time_min1.633333333333335
traveled_tiles_max14
traveled_tiles_mean6.6
traveled_tiles_median4
traveled_tiles_min3
valid_direction_max2.499999999999991
valid_direction_mean1.193333333333332
valid_direction_median0.966666666666664
valid_direction_min0.6333333333333352
143281379trimcaoBaseline solution using imitation learning from logsaido1_LF1_r3-v3step4-vizsuccessyes3740:01:18(hidden)
driven_lanedir_median0.7289404183237616
deviation-center-line_median0.1767901075242912
in-drivable-lane_median0


other stats
deviation-center-line_max0.25641968601091086
deviation-center-line_mean0.15213430965611768
deviation-center-line_min0.020940605101197066
deviation-heading_max0.563544747176043
deviation-heading_mean0.3743678228676147
deviation-heading_median0.4235821644002892
deviation-heading_min0.1533850517777587
driven_any_max1.153039826967453
driven_any_mean0.943396222730158
driven_any_median0.9783368237121248
driven_any_min0.7104588837195143
driven_lanedir_max1.1164095863188164
driven_lanedir_mean0.7064681007018854
driven_lanedir_min0.003943222131212565
in-drivable-lane_max2.8
in-drivable-lane_mean0.5599999999999999
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.094805493606451, "driven_lanedir": 0.003943222131212565, "in-drivable-lane": 2.8, "deviation-heading": 0.563544747176043, "deviation-center-line": 0.020940605101197066}, "ep001": {"driven_any": 0.7104588837195143, "driven_lanedir": 0.7084938889760615, "in-drivable-lane": 0, "deviation-heading": 0.1533850517777587, "deviation-center-line": 0.1767901075242912}, "ep002": {"driven_any": 0.780340085645246, "driven_lanedir": 0.7289404183237616, "in-drivable-lane": 0, "deviation-heading": 0.5429234369727753, "deviation-center-line": 0.06884595755779799}, "ep003": {"driven_any": 0.9783368237121248, "driven_lanedir": 0.9745533877595742, "in-drivable-lane": 0, "deviation-heading": 0.18840371401120745, "deviation-center-line": 0.25641968601091086}, "ep004": {"driven_any": 1.153039826967453, "driven_lanedir": 1.1164095863188164, "in-drivable-lane": 0, "deviation-heading": 0.4235821644002892, "deviation-center-line": 0.2376751920863914}}
143241378melorianTensorflow templateaido1_LF1_r3-v3step4-vizsuccessyes3740:03:07(hidden)
driven_lanedir_median4.860168747083806
deviation-center-line_median0.5177261330238835
in-drivable-lane_median0.5000000000000007


other stats
deviation-center-line_max0.9871675545715244
deviation-center-line_mean0.5324138078256986
deviation-center-line_min0.17881747384937827
deviation-heading_max2.9948998980375268
deviation-heading_mean2.1226150535590556
deviation-heading_median2.1908094437128325
deviation-heading_min1.087158950713311
driven_any_max8.626687596743585
driven_any_mean5.398822816088453
driven_any_median5.7543979785933415
driven_any_min1.2714029240497298
driven_lanedir_max7.955102455468161
driven_lanedir_mean4.719230857114978
driven_lanedir_min1.1094450122785178
in-drivable-lane_max1.2333333333333318
in-drivable-lane_mean0.6266666666666663
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 5.803722363200518, "driven_lanedir": 4.860168747083806, "in-drivable-lane": 1.033333333333332, "deviation-heading": 2.1908094437128325, "deviation-center-line": 0.5177261330238835}, "ep001": {"driven_any": 5.537903217855085, "driven_lanedir": 4.575171984923749, "in-drivable-lane": 1.2333333333333318, "deviation-heading": 2.0604453764787127, "deviation-center-line": 0.4536673648475869}, "ep002": {"driven_any": 8.626687596743585, "driven_lanedir": 7.955102455468161, "in-drivable-lane": 0.366666666666666, "deviation-heading": 2.9948998980375268, "deviation-center-line": 0.9871675545715244}, "ep003": {"driven_any": 5.7543979785933415, "driven_lanedir": 5.096266085820653, "in-drivable-lane": 0.5000000000000007, "deviation-heading": 2.2797615988528945, "deviation-center-line": 0.5246905128361194}, "ep004": {"driven_any": 1.2714029240497298, "driven_lanedir": 1.1094450122785178, "in-drivable-lane": 0, "deviation-heading": 1.087158950713311, "deviation-center-line": 0.17881747384937827}}
143201378melorianTensorflow templateaido1_LF1_r3-v3step1-simulationsuccessyes3740:02:34(hidden)
other stats
simulation-passed1
143191377miksazRL solutionaido1_LF1_r3-v3step4-vizsuccessyes3740:02:06(hidden)
driven_lanedir_median0.20721960092560376
deviation-center-line_median0.19081137319122096
in-drivable-lane_median1.4666666666666668


other stats
deviation-center-line_max1.213988629795965
deviation-center-line_mean0.3872606337007666
deviation-center-line_min0.06503287605289823
deviation-heading_max6.297782103523679
deviation-heading_mean1.9935461513390351
deviation-heading_median0.975427594961353
deviation-heading_min0.3975602834496309
driven_any_max19.609308601120446
driven_any_mean6.56077823863568
driven_any_median3.7570576076703905
driven_any_min2.253839732459414
driven_lanedir_max1.571245411088923
driven_lanedir_mean0.23245865798318135
driven_lanedir_min-0.8544299929048638
in-drivable-lane_max9.533333333333326
in-drivable-lane_mean2.8333333333333317
in-drivable-lane_min0.4333333333333318
per-episodes
details{"ep000": {"driven_any": 2.471828970219067, "driven_lanedir": 0.20721960092560376, "in-drivable-lane": 1.066666666666666, "deviation-heading": 0.9337757840801802, "deviation-center-line": 0.19081137319122096}, "ep001": {"driven_any": 2.253839732459414, "driven_lanedir": -0.25718237190288384, "in-drivable-lane": 1.6666666666666683, "deviation-heading": 0.3975602834496309, "deviation-center-line": 0.06503287605289823}, "ep002": {"driven_any": 4.711856281709082, "driven_lanedir": 1.571245411088923, "in-drivable-lane": 1.4666666666666668, "deviation-heading": 1.3631849906803344, "deviation-center-line": 0.284553929937309}, "ep003": {"driven_any": 19.609308601120446, "driven_lanedir": -0.8544299929048638, "in-drivable-lane": 9.533333333333326, "deviation-heading": 6.297782103523679, "deviation-center-line": 1.213988629795965}, "ep004": {"driven_any": 3.7570576076703905, "driven_lanedir": 0.495440642709128, "in-drivable-lane": 0.4333333333333318, "deviation-heading": 0.975427594961353, "deviation-center-line": 0.18191635952643953}}
143181377miksazRL solutionaido1_LF1_r3-v3step3-videossuccessyes3740:01:23(hidden)
other stats
videos1
143061370mpicquetJava templateaido1_amod_service_quality_r1-v3step1-simulationerroryes3740:31:24
Timeout: Waited 180 [...]
Timeout:

Waited 1800.49716687 for container to finish. Giving up. 
(hidden)
143031367heyt0nySAIC MOSCOW MMLaido1_LF1_r3-v3step1-simulationfailedyes3740:00:37
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 124, in run
    solve(params, cis)
  File "solution.py", line 85, in solve
    cis.info('Logging. %s %s %s ' % (str(env.unwrapped.robot_speed), str(env.unwrapped.cur_pos), str(env.unwrapped.cur_angle)))
AttributeError: 'SimpleSimAgentEnv' object has no attribute 'robot_speed'


(hidden)
143021366miksazRL solutionaido1_LF1_r3-v3step1-simulationerroryes3740:00:55
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
143011365miksazRL solutionaido1_LF1_r3-v3step1-simulationerroryes3740:00:53
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
142981363tongqiusuiTensorflow templateaido1_LF1_r3-v3step3-videossuccessyes3740:00:58(hidden)
other stats
videos1
142971363tongqiusuiTensorflow templateaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median9.266666666666644


other stats
episodes
details{"ep000": {"nsteps": 255, "reward": -3.2289562618076872, "good_angle": 1.1346931930881636, "survival_time": 8.49999999999998, "traveled_tiles": 11, "valid_direction": 1.933333333333329}, "ep001": {"nsteps": 385, "reward": -1.9227435327679303, "good_angle": 0.9429759282318948, "survival_time": 12.833333333333298, "traveled_tiles": 15, "valid_direction": 1.6999999999999953}, "ep002": {"nsteps": 411, "reward": -1.807526985767984, "good_angle": 0.7247250775367927, "survival_time": 13.699999999999962, "traveled_tiles": 14, "valid_direction": 1.166666666666664}, "ep003": {"nsteps": 278, "reward": -2.957609235784776, "good_angle": 0.7534854144834433, "survival_time": 9.266666666666644, "traveled_tiles": 9, "valid_direction": 0.9999999999999972}, "ep004": {"nsteps": 70, "reward": -13.692152492489134, "good_angle": 0.5108653120693448, "survival_time": 2.333333333333335, "traveled_tiles": 3, "valid_direction": 0.4666666666666657}}
good_angle_max1.1346931930881636
good_angle_mean0.8133489850819279
good_angle_median0.7534854144834433
good_angle_min0.5108653120693448
reward_max-1.807526985767984
reward_mean-4.721797701723502
reward_median-2.957609235784776
reward_min-13.692152492489134
survival_time_max13.699999999999962
survival_time_mean9.326666666666643
survival_time_min2.333333333333335
traveled_tiles_max15
traveled_tiles_mean10.4
traveled_tiles_median11
traveled_tiles_min3
valid_direction_max1.933333333333329
valid_direction_mean1.2533333333333303
valid_direction_median1.166666666666664
valid_direction_min0.4666666666666657
142861353dcharrezTensorflow Sagemaker templateaido1_LF1_r3-v3step1-simulationfailedyes3740:04:54
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 86, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 33, in solve
    graph_location='tf_models/')  # this is the folder where our models are stored.
  File "/workspace/model.py", line 60, in __init__
    self._initialize(observation_shape, action_shape, graph_location)
  File "/workspace/model.py", line 84, in _initialize
    self._storing(storage_location)
  File "/workspace/model.py", line 105, in _storing
    raise IOError('No model found...')
IOError: No model found...


(hidden)
142591337alezanatest python3aido1_amod_fleet_size_r1-v3step2-scoringsuccessyes3740:00:20(hidden)
fleet_size-1000000000


other stats
efficiency-71.54442699108064
service_quality-34.353026747770066
142531333tongqiusuiNvidia CNNaido1_LF1_r3-v3step1-simulationerroryes3740:01:05
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
142481330Dzenan LapandicLFV bd testaido1_LFV_r1-v3step2-scoringsuccessyes3740:00:30(hidden)
survival_time_median3.73333333333333


other stats
episodes
details{"ep000": {"nsteps": 459, "reward": -3.835491883339186, "good_angle": 31.280266495985295, "survival_time": 15.299999999999956, "traveled_tiles": 9, "valid_direction": 3.3333333333333215}, "ep001": {"nsteps": 73, "reward": -13.225716316102917, "good_angle": 1.10822910162206, "survival_time": 2.4333333333333345, "traveled_tiles": 3, "valid_direction": 0.4333333333333318}, "ep002": {"nsteps": 279, "reward": -3.0696508528724795, "good_angle": 0.03802610175985486, "survival_time": 9.299999999999978, "traveled_tiles": 6, "valid_direction": 0}, "ep003": {"nsteps": 112, "reward": -8.283359774093176, "good_angle": 0.7893557730201478, "survival_time": 3.73333333333333, "traveled_tiles": 4, "valid_direction": 0.4999999999999982}, "ep004": {"nsteps": 76, "reward": -12.753283822847726, "good_angle": 1.0729374844946031, "survival_time": 2.533333333333334, "traveled_tiles": 3, "valid_direction": 0.3999999999999986}}
good_angle_max31.280266495985295
good_angle_mean6.857762991376392
good_angle_median1.0729374844946031
good_angle_min0.03802610175985486
reward_max-3.0696508528724795
reward_mean-8.233500529851096
reward_median-8.283359774093176
reward_min-13.225716316102917
survival_time_max15.299999999999956
survival_time_mean6.659999999999987
survival_time_min2.4333333333333345
traveled_tiles_max9
traveled_tiles_mean5
traveled_tiles_median4
traveled_tiles_min3
valid_direction_max3.3333333333333215
valid_direction_mean0.93333333333333
valid_direction_median0.4333333333333318
valid_direction_min0
142441327Dzenan LapandicLFV bd testaido1_LFV_r1-v3step1-simulationerroryes3740:00:45
InvalidEvaluator: Tr [...]
InvalidEvaluator:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 488, in wrap_evaluator
    evaluator.score(cie)
  File "eval.py", line 97, in score
    raise dc.InvalidEvaluator(msg)
InvalidEvaluator: Gym exited with code 2
(hidden)
14228195Florian GolemoPyTorch DDPG templateaido1_LF1_r3-v3step1-simulationerroryes3740:30:43
Timeout: Waited 180 [...]
Timeout:

Waited 1802.52906895 for container to finish. Giving up. 
(hidden)
14226217Bhairav MehtaROS-based Lane Followingaido1_LF1_r3-v3step1-simulationfailedyes3740:00:37
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Env Duckietown-Lf-Lfv-Navv-Silent-v1 not found (valid versions include ['Duckietown-Lf-Lfv-Navv-Silent-v0'])
(hidden)
14221765Andrea CensiRandom executionaido1_LF1_r3-v3step3-videossuccessyes3740:00:42(hidden)
other stats
videos1
14218765Andrea CensiRandom executionaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:22(hidden)
survival_time_median2.066666666666669


other stats
episodes
details{"ep000": {"nsteps": 80, "reward": -13.146168287156616, "good_angle": 0.988596111334971, "survival_time": 2.666666666666667, "traveled_tiles": 2, "valid_direction": 0.966666666666665}, "ep001": {"nsteps": 52, "reward": -18.768880833943303, "good_angle": 0.01839792651132736, "survival_time": 1.7333333333333354, "traveled_tiles": 2, "valid_direction": 0}, "ep002": {"nsteps": 59, "reward": -16.235870020037865, "good_angle": 0.3384250980712922, "survival_time": 1.9666666666666697, "traveled_tiles": 2, "valid_direction": 0.6333333333333353}, "ep003": {"nsteps": 62, "reward": -15.791676875759638, "good_angle": 0.0515737005933409, "survival_time": 2.066666666666669, "traveled_tiles": 2, "valid_direction": 0.13333333333333308}, "ep004": {"nsteps": 84, "reward": -11.531729141095033, "good_angle": 0.3145791656573848, "survival_time": 2.8, "traveled_tiles": 3, "valid_direction": 0.6333333333333311}}
good_angle_max0.988596111334971
good_angle_mean0.3423144004336633
good_angle_median0.3145791656573848
good_angle_min0.01839792651132736
reward_max-11.531729141095033
reward_mean-15.094865031598491
reward_median-15.791676875759638
reward_min-18.768880833943303
survival_time_max2.8
survival_time_mean2.2466666666666684
survival_time_min1.7333333333333354
traveled_tiles_max3
traveled_tiles_mean2.2
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max0.966666666666665
valid_direction_mean0.4733333333333329
valid_direction_median0.6333333333333311
valid_direction_min0
14212508JonJP pipelineaido1_LF1_r3-v3step4-vizsuccessyes3740:01:27(hidden)
driven_lanedir_median0.1768261762346901
deviation-center-line_median0.0285248415026004
in-drivable-lane_median1.2666666666666673


other stats
deviation-center-line_max0.5260007044060113
deviation-center-line_mean0.1308319691883515
deviation-center-line_min0
deviation-heading_max8.358966045240344
deviation-heading_mean1.8905642959121909
deviation-heading_median0.2298559904620248
deviation-heading_min0
driven_any_max0.5845931997397718
driven_any_mean0.49328223957779144
driven_any_median0.5776448573127475
driven_any_min0.2566032596697516
driven_lanedir_max0.18331129675017263
driven_lanedir_mean0.1317139831321698
driven_lanedir_min0
in-drivable-lane_max4.533333333333326
in-drivable-lane_mean2.0266666666666646
in-drivable-lane_min0.2333333333333333
per-episodes
details{"ep000": {"driven_any": 0.2566032596697516, "driven_lanedir": 0, "in-drivable-lane": 1.2666666666666673, "deviation-heading": 0, "deviation-center-line": 0}, "ep001": {"driven_any": 0.5844087063065192, "driven_lanedir": 0.1768261762346901, "in-drivable-lane": 3.7999999999999954, "deviation-heading": 0.1853799589947491, "deviation-center-line": 0.022460208781519893}, "ep002": {"driven_any": 0.463161174860167, "driven_lanedir": 0.11789858852570888, "in-drivable-lane": 0.2333333333333333, "deviation-heading": 8.358966045240344, "deviation-center-line": 0.5260007044060113}, "ep003": {"driven_any": 0.5776448573127475, "driven_lanedir": 0.18331129675017263, "in-drivable-lane": 0.3, "deviation-heading": 0.6786194848638363, "deviation-center-line": 0.07717409125162582}, "ep004": {"driven_any": 0.5845931997397718, "driven_lanedir": 0.18053385415027745, "in-drivable-lane": 4.533333333333326, "deviation-heading": 0.2298559904620248, "deviation-center-line": 0.0285248415026004}}
14207508JonJP pipelineaido1_LF1_r3-v3step3-videossuccessyes3740:00:57(hidden)
other stats
videos1
14202509Dzenan LapandicBaseline solution using imitation learning from logsaido1_LF1_r3-v3step1-simulationfailedyes3740:00:38
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 120, in run
    raise InvalidSubmission(str(e))
InvalidSubmission: can't multiply sequence by non-int of type 'float'

(hidden)
14196507JonJP pipelineaido1_LF1_r3-v3step4-vizsuccessyes3740:01:33(hidden)
driven_lanedir_median0.09214455109415363
deviation-center-line_median0.009179842381599752
in-drivable-lane_median4.533333333333326


other stats
deviation-center-line_max0.5796633783410841
deviation-center-line_mean0.1209098121585994
deviation-center-line_min0
deviation-heading_max8.853841885288634
deviation-heading_mean1.8574118411383056
deviation-heading_median0.15086978677159554
deviation-heading_min0
driven_any_max0.4811032074290481
driven_any_mean0.38035187058526465
driven_any_median0.406228046310888
driven_any_min0.23273485242025824
driven_lanedir_max0.1622245330469001
driven_lanedir_mean0.08651723688144282
driven_lanedir_min0
in-drivable-lane_max5.29999999999999
in-drivable-lane_mean3.4533333333333283
in-drivable-lane_min0.09999999999999998
per-episodes
details{"ep000": {"driven_any": 0.23273485242025824, "driven_lanedir": 0, "in-drivable-lane": 2.4666666666666677, "deviation-heading": 0, "deviation-center-line": 0}, "ep001": {"driven_any": 0.406228046310888, "driven_lanedir": 0.09503714041736178, "in-drivable-lane": 4.866666666666658, "deviation-heading": 0.1928773481398994, "deviation-center-line": 0.009179842381599752}, "ep002": {"driven_any": 0.4811032074290481, "driven_lanedir": 0.1622245330469001, "in-drivable-lane": 0.09999999999999998, "deviation-heading": 8.853841885288634, "deviation-center-line": 0.5796633783410841}, "ep003": {"driven_any": 0.4114781437844365, "driven_lanedir": 0.09214455109415363, "in-drivable-lane": 4.533333333333326, "deviation-heading": 0.15086978677159554, "deviation-center-line": 0.0062512205733389935}, "ep004": {"driven_any": 0.3702151029816927, "driven_lanedir": 0.08317995984879856, "in-drivable-lane": 5.29999999999999, "deviation-heading": 0.08947018549139796, "deviation-center-line": 0.009454619496974176}}
14193507JonJP pipelineaido1_LF1_r3-v3step3-videossuccessyes3740:00:57(hidden)
other stats
videos1
14186506JonJP pipelineaido1_LF1_r3-v3step4-vizsuccessyes3740:01:32(hidden)
driven_lanedir_median0.09214455109415363
deviation-center-line_median0.009179842381599752
in-drivable-lane_median4.533333333333326


other stats
deviation-center-line_max0.5796633783410841
deviation-center-line_mean0.1209098121585994
deviation-center-line_min0
deviation-heading_max8.853841885288634
deviation-heading_mean1.8574118411383056
deviation-heading_median0.15086978677159554
deviation-heading_min0
driven_any_max0.4811032074290481
driven_any_mean0.3846696233192129
driven_any_median0.406228046310888
driven_any_min0.2543236160899993
driven_lanedir_max0.1622245330469001
driven_lanedir_mean0.08651723688144282
driven_lanedir_min0
in-drivable-lane_max5.29999999999999
in-drivable-lane_mean3.179999999999995
in-drivable-lane_min0.09999999999999998
per-episodes
details{"ep000": {"driven_any": 0.2543236160899993, "driven_lanedir": 0, "in-drivable-lane": 1.1, "deviation-heading": 0, "deviation-center-line": 0}, "ep001": {"driven_any": 0.406228046310888, "driven_lanedir": 0.09503714041736178, "in-drivable-lane": 4.866666666666658, "deviation-heading": 0.1928773481398994, "deviation-center-line": 0.009179842381599752}, "ep002": {"driven_any": 0.4811032074290481, "driven_lanedir": 0.1622245330469001, "in-drivable-lane": 0.09999999999999998, "deviation-heading": 8.853841885288634, "deviation-center-line": 0.5796633783410841}, "ep003": {"driven_any": 0.4114781437844365, "driven_lanedir": 0.09214455109415363, "in-drivable-lane": 4.533333333333326, "deviation-heading": 0.15086978677159554, "deviation-center-line": 0.0062512205733389935}, "ep004": {"driven_any": 0.3702151029816927, "driven_lanedir": 0.08317995984879856, "in-drivable-lane": 5.29999999999999, "deviation-heading": 0.08947018549139796, "deviation-center-line": 0.009454619496974176}}
14174506JonJP pipelineaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:23(hidden)
survival_time_median5.166666666666658


other stats
episodes
details{"ep000": {"nsteps": 34, "reward": -32.310612480430045, "good_angle": 2.0415144737904436, "survival_time": 1.1333333333333335, "traveled_tiles": 1, "valid_direction": 1.0333333333333334}, "ep001": {"nsteps": 155, "reward": -9.0619213102806, "good_angle": 5.732044765967412, "survival_time": 5.166666666666658, "traveled_tiles": 1, "valid_direction": 4.999999999999991}, "ep002": {"nsteps": 214, "reward": -5.117444511885955, "good_angle": 11.53068705601369, "survival_time": 7.133333333333318, "traveled_tiles": 1, "valid_direction": 7.06666666666665}, "ep003": {"nsteps": 144, "reward": -9.578221540328943, "good_angle": 5.3039926200775325, "survival_time": 4.799999999999993, "traveled_tiles": 2, "valid_direction": 4.633333333333326}, "ep004": {"nsteps": 165, "reward": -8.702814062617042, "good_angle": 5.138312815742137, "survival_time": 5.49999999999999, "traveled_tiles": 2, "valid_direction": 5.3666666666666565}}
good_angle_max11.53068705601369
good_angle_mean5.949310346318243
good_angle_median5.3039926200775325
good_angle_min2.0415144737904436
reward_max-5.117444511885955
reward_mean-12.954202781108515
reward_median-9.0619213102806
reward_min-32.310612480430045
survival_time_max7.133333333333318
survival_time_mean4.746666666666658
survival_time_min1.1333333333333335
traveled_tiles_max2
traveled_tiles_mean1.4
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max7.06666666666665
valid_direction_mean4.619999999999991
valid_direction_median4.999999999999991
valid_direction_min1.0333333333333334
14166506JonJP pipelineaido1_LF1_r3-v3step1-simulationsuccessyes3740:02:39(hidden)
other stats
simulation-passed1
14161505Dzenan LapandicBaseline solution using imitation learning from logsaido1_LF1_r3-v3step1-simulationfailedyes3740:00:37
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 88, in run
    raise InvalidSubmission(str(e))
InvalidSubmission: index 1 is out of bounds for axis 1 with size 1

(hidden)
14153503JonJP pipelineaido1_LF1_r3-v3step4-vizsuccessyes3740:01:58(hidden)
driven_lanedir_median0.42227261567306407
deviation-center-line_median0.08128981096332001
in-drivable-lane_median3.3999999999999977


other stats
deviation-center-line_max0.13026589242752282
deviation-center-line_mean0.07317017789440536
deviation-center-line_min0
deviation-heading_max0.4556120586028843
deviation-heading_mean0.3372425737137796
deviation-heading_median0.3988180652546159
deviation-heading_min0
driven_any_max5.304125776697546
driven_any_mean1.669494843993854
driven_any_median0.8905392926537553
driven_any_min0.26038634664305227
driven_lanedir_max0.5814875869893381
driven_lanedir_mean0.33016805775043817
driven_lanedir_min0
in-drivable-lane_max14.599999999999952
in-drivable-lane_mean6.459999999999984
in-drivable-lane_min1.1333333333333355
per-episodes
details{"ep000": {"driven_any": 0.26038634664305227, "driven_lanedir": 0, "in-drivable-lane": 3.3999999999999977, "deviation-heading": 0, "deviation-center-line": 0}, "ep001": {"driven_any": 1.0430997801399369, "driven_lanedir": 0.5814875869893381, "in-drivable-lane": 1.1999999999999995, "deviation-heading": 0.39555103215797793, "deviation-center-line": 0.13026589242752282}, "ep002": {"driven_any": 5.304125776697546, "driven_lanedir": 0.19574528279350775, "in-drivable-lane": 11.966666666666631, "deviation-heading": 0.43623171255341986, "deviation-center-line": 0.05977368650864371}, "ep003": {"driven_any": 0.8493230238349783, "driven_lanedir": 0.42227261567306407, "in-drivable-lane": 1.1333333333333355, "deviation-heading": 0.3988180652546159, "deviation-center-line": 0.08128981096332001}, "ep004": {"driven_any": 0.8905392926537553, "driven_lanedir": 0.45133480329628095, "in-drivable-lane": 14.599999999999952, "deviation-heading": 0.4556120586028843, "deviation-center-line": 0.09452149957254032}}
14142502Liam PaullTensorflow templateaido1_LF1_r3-v3step4-vizsuccessyes3740:03:11(hidden)
driven_lanedir_median5.302566481656299
deviation-center-line_median0.5565671916823862
in-drivable-lane_median0.16666666666666652


other stats
deviation-center-line_max0.8859936557711611
deviation-center-line_mean0.528607812747053
deviation-center-line_min0.16606102536010822
deviation-heading_max3.033370854841319
deviation-heading_mean2.280705656084282
deviation-heading_median2.4561990578057573
deviation-heading_min0.9882049312556496
driven_any_max9.0481182555653
driven_any_mean6.141090110593249
driven_any_median5.90889969385339
driven_any_min1.2967673217013216
driven_lanedir_max8.21557018025004
driven_lanedir_mean5.398798112513221
driven_lanedir_min1.1127930583588117
in-drivable-lane_max1.433333333333331
in-drivable-lane_mean0.5466666666666657
in-drivable-lane_min0.03333333333333344
per-episodes
details{"ep000": {"driven_any": 5.90889969385339, "driven_lanedir": 4.810083815920299, "in-drivable-lane": 0.9666666666666656, "deviation-heading": 2.4510219442237524, "deviation-center-line": 0.44833422712128373}, "ep001": {"driven_any": 9.0481182555653, "driven_lanedir": 7.552977026380654, "in-drivable-lane": 1.433333333333331, "deviation-heading": 2.4747314922949313, "deviation-center-line": 0.5565671916823862}, "ep002": {"driven_any": 8.688836269603568, "driven_lanedir": 8.21557018025004, "in-drivable-lane": 0.13333333333333308, "deviation-heading": 3.033370854841319, "deviation-center-line": 0.8859936557711611}, "ep003": {"driven_any": 5.762829012242665, "driven_lanedir": 5.302566481656299, "in-drivable-lane": 0.16666666666666652, "deviation-heading": 2.4561990578057573, "deviation-center-line": 0.5860829638003254}, "ep004": {"driven_any": 1.2967673217013216, "driven_lanedir": 1.1127930583588117, "in-drivable-lane": 0.03333333333333344, "deviation-heading": 0.9882049312556496, "deviation-center-line": 0.16606102536010822}}
141261014DavidPytorch ILaido1_LF1_r3-v3step1-simulationsuccessyes3740:07:07(hidden)
other stats
simulation-passed1
141181012DavidPytorch ILaido1_LF1_r3-v3step4-vizsuccessyes3740:01:34(hidden)
driven_lanedir_median2.698483872000147
deviation-center-line_median0.1141705820433344
in-drivable-lane_median0.09999999999999988


other stats
deviation-center-line_max0.2746417194998002
deviation-center-line_mean0.15867932379677854
deviation-center-line_min0.06264226358418436
deviation-heading_max1.237071973429899
deviation-heading_mean0.6928635446867579
deviation-heading_median0.7270644762155613
deviation-heading_min0.25554791073930605
driven_any_max6.67258745930385
driven_any_mean4.036291644438568
driven_any_median3.729364788911458
driven_any_min1.4387120515518923
driven_lanedir_max6.422034032605247
driven_lanedir_mean3.564472827295842
driven_lanedir_min1.3545319446031665
in-drivable-lane_max0.8666666666666666
in-drivable-lane_mean0.24666666666666667
in-drivable-lane_min0.033333333333333326
per-episodes
details{"ep000": {"driven_any": 3.729364788911458, "driven_lanedir": 2.698483872000147, "in-drivable-lane": 0.8666666666666666, "deviation-heading": 0.4444425066343305, "deviation-center-line": 0.09417733573056712}, "ep001": {"driven_any": 1.4387120515518923, "driven_lanedir": 1.3545319446031665, "in-drivable-lane": 0.033333333333333326, "deviation-heading": 0.25554791073930605, "deviation-center-line": 0.06264226358418436}, "ep002": {"driven_any": 6.67258745930385, "driven_lanedir": 6.422034032605247, "in-drivable-lane": 0.09999999999999988, "deviation-heading": 0.7270644762155613, "deviation-center-line": 0.24776471812600656}, "ep003": {"driven_any": 6.438878588574458, "driven_lanedir": 5.911383048849606, "in-drivable-lane": 0.09999999999999976, "deviation-heading": 1.237071973429899, "deviation-center-line": 0.2746417194998002}, "ep004": {"driven_any": 1.9019153338511805, "driven_lanedir": 1.435931238421042, "in-drivable-lane": 0.13333333333333364, "deviation-heading": 0.8001908564146925, "deviation-center-line": 0.1141705820433344}}
141091012DavidPytorch ILaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:22(hidden)
survival_time_median3.3666666666666645


other stats
episodes
details{"ep000": {"nsteps": 101, "reward": -9.51644176429156, "good_angle": 0.1786445516274026, "survival_time": 3.3666666666666645, "traveled_tiles": 6, "valid_direction": 0.566666666666666}, "ep001": {"nsteps": 41, "reward": -23.707647001756943, "good_angle": 0.04579691577208957, "survival_time": 1.3666666666666676, "traveled_tiles": 3, "valid_direction": 0.06666666666666687}, "ep002": {"nsteps": 174, "reward": -4.994314238723453, "good_angle": 0.07054867910858756, "survival_time": 5.799999999999989, "traveled_tiles": 11, "valid_direction": 0.06666666666666643}, "ep003": {"nsteps": 170, "reward": -5.276598060631317, "good_angle": 1.1137454422165698, "survival_time": 5.666666666666656, "traveled_tiles": 11, "valid_direction": 0.5333333333333314}, "ep004": {"nsteps": 52, "reward": -18.770169389102826, "good_angle": 0.9017961787199076, "survival_time": 1.7333333333333354, "traveled_tiles": 4, "valid_direction": 0.6000000000000019}}
good_angle_max1.1137454422165698
good_angle_mean0.4621063534889115
good_angle_median0.1786445516274026
good_angle_min0.04579691577208957
reward_max-4.994314238723453
reward_mean-12.45303409090122
reward_median-9.51644176429156
reward_min-23.707647001756943
survival_time_max5.799999999999989
survival_time_mean3.5866666666666625
survival_time_min1.3666666666666676
traveled_tiles_max11
traveled_tiles_mean7
traveled_tiles_median6
traveled_tiles_min3
valid_direction_max0.6000000000000019
valid_direction_mean0.3666666666666666
valid_direction_median0.5333333333333314
valid_direction_min0.06666666666666643
14099756marshinPyTorch templateaido1_LF1_r3-v3step1-simulationsuccessyes3740:03:56(hidden)
other stats
simulation-passed1
140871011sodabetastay youngaido1_LF1_r3-v3step1-simulationfailedyes3740:00:43
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 127, in run
    solve(params, cis)
  File "solution.py", line 67, in solve
    from model import A2CPG
  File "/workspace/model.py", line 5, in <module>
    import torch
ModuleNotFoundError: No module named 'torch'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 127, in run
    solve(params, cis)
  File "solution.py", line 67, in solve
    from model import A2CPG
  File "/workspace/model.py", line 5, in <module>
    import torch
ModuleNotFoundError: No module named 'torch'


(hidden)
140811264gunshiROS-based Lane Followingaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median1.3000000000000007


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.04406096532439915, "good_angle": 1.6190369783012328, "survival_time": 16.666666666666654, "traveled_tiles": 10, "valid_direction": 3.533333333333347}, "ep001": {"nsteps": 36, "reward": -27.487091691440177, "good_angle": 0.10551522272941194, "survival_time": 1.2000000000000004, "traveled_tiles": 2, "valid_direction": 0.23333333333333373}, "ep002": {"nsteps": 39, "reward": -25.38531202937548, "good_angle": 0.08613808007381446, "survival_time": 1.3000000000000007, "traveled_tiles": 2, "valid_direction": 0.3000000000000002}, "ep003": {"nsteps": 500, "reward": 0.12507430091965943, "good_angle": 2.3139292651912995, "survival_time": 16.666666666666654, "traveled_tiles": 10, "valid_direction": 3.333333333333356}, "ep004": {"nsteps": 12, "reward": -83.22223793715239, "good_angle": 0.1948223704921141, "survival_time": 0.4, "traveled_tiles": 2, "valid_direction": 0.2}}
good_angle_max2.3139292651912995
good_angle_mean0.8638883833575746
good_angle_median0.1948223704921141
good_angle_min0.08613808007381446
reward_max0.12507430091965943
reward_mean-27.185101278344796
reward_median-25.38531202937548
reward_min-83.22223793715239
survival_time_max16.666666666666654
survival_time_mean7.246666666666661
survival_time_min0.4
traveled_tiles_max10
traveled_tiles_mean5.2
traveled_tiles_median2
traveled_tiles_min2
valid_direction_max3.533333333333347
valid_direction_mean1.5200000000000076
valid_direction_median0.3000000000000002
valid_direction_min0.2
14078495heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step4-vizsuccessyes3740:04:47(hidden)
driven_lanedir_median-2.1165783877323467
deviation-center-line_median0.2194657911412292
in-drivable-lane_median8.299999999999994


other stats
deviation-center-line_max0.5309285294557311
deviation-center-line_mean0.275015305868552
deviation-center-line_min0.13659227029518645
deviation-heading_max6.5852025001888945
deviation-heading_mean6.542339066080227
deviation-heading_median6.573868260014544
deviation-heading_min6.482843321680566
driven_any_max6.89674394349743
driven_any_mean6.89674394349742
driven_any_median6.8967439434974205
driven_any_min6.896743943497403
driven_lanedir_max-2.1132848614892374
driven_lanedir_mean-2.1177606848093142
driven_lanedir_min-2.124505824870274
in-drivable-lane_max8.36666666666666
in-drivable-lane_mean8.319999999999991
in-drivable-lane_min8.299999999999992
per-episodes
details{"ep000": {"driven_any": 6.8967439434974205, "driven_lanedir": -2.124505824870274, "in-drivable-lane": 8.333333333333325, "deviation-heading": 6.491552752250759, "deviation-center-line": 0.1729146796828003}, "ep001": {"driven_any": 6.896743943497425, "driven_lanedir": -2.1163184210916683, "in-drivable-lane": 8.36666666666666, "deviation-heading": 6.482843321680566, "deviation-center-line": 0.2194657911412292}, "ep002": {"driven_any": 6.89674394349743, "driven_lanedir": -2.1165783877323467, "in-drivable-lane": 8.299999999999994, "deviation-heading": 6.573868260014544, "deviation-center-line": 0.13659227029518645}, "ep003": {"driven_any": 6.896743943497418, "driven_lanedir": -2.1132848614892374, "in-drivable-lane": 8.299999999999994, "deviation-heading": 6.5852025001888945, "deviation-center-line": 0.3151752587678129}, "ep004": {"driven_any": 6.896743943497403, "driven_lanedir": -2.1181159288630447, "in-drivable-lane": 8.299999999999992, "deviation-heading": 6.578228496266371, "deviation-center-line": 0.5309285294557311}}
14069495heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step3-videossuccessyes3740:02:08(hidden)
other stats
videos1
140581262Bhairav MehtaROS-based Lane Followingaido1_LF1_r3-v3step3-videossuccessyes3740:01:11(hidden)
other stats
videos1
140571262Bhairav MehtaROS-based Lane Followingaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:25(hidden)
survival_time_median1.2333333333333338


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.007248003905871883, "good_angle": 1.5000937400028649, "survival_time": 16.666666666666654, "traveled_tiles": 10, "valid_direction": 3.100000000000015}, "ep001": {"nsteps": 57, "reward": -17.246367487010726, "good_angle": 0.0996527140182342, "survival_time": 1.9000000000000024, "traveled_tiles": 2, "valid_direction": 0.10000000000000032}, "ep002": {"nsteps": 14, "reward": -71.231668387706, "good_angle": 0.2101786855918144, "survival_time": 0.4666666666666666, "traveled_tiles": 1, "valid_direction": 0.3666666666666666}, "ep003": {"nsteps": 37, "reward": -26.82068298666461, "good_angle": 0.11366433959695936, "survival_time": 1.2333333333333338, "traveled_tiles": 1, "valid_direction": 0.20000000000000032}, "ep004": {"nsteps": 26, "reward": -38.12057849908104, "good_angle": 0.15600552808828816, "survival_time": 0.8666666666666666, "traveled_tiles": 1, "valid_direction": 0.19999999999999996}}
good_angle_max1.5000937400028649
good_angle_mean0.4159190014596321
good_angle_median0.15600552808828816
good_angle_min0.0996527140182342
reward_max0.007248003905871883
reward_mean-30.6824098713113
reward_median-26.82068298666461
reward_min-71.231668387706
survival_time_max16.666666666666654
survival_time_mean4.226666666666665
survival_time_min0.4666666666666666
traveled_tiles_max10
traveled_tiles_mean3
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max3.100000000000015
valid_direction_mean0.7933333333333363
valid_direction_median0.20000000000000032
valid_direction_min0.10000000000000032
14056752miksazPyTorch DDPG templateaido1_LF1_r3-v3step1-simulationfailedyes3740:00:38
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 104, in run
    solve(params, cis)
  File "solution.py", line 29, in solve
    from wrappers import NormalizeWrapper, ImgWrapper, ActionWrapper, ResizeWrapper
ImportError: cannot import name NormalizeWrapper


(hidden)
14054494heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step4-vizsuccessyes3740:00:51(hidden)
driven_lanedir_median0.7583624342945385
deviation-center-line_median0.05856940306194948
in-drivable-lane_median0.033333333333333326


other stats
deviation-center-line_max0.0848318451919554
deviation-center-line_mean0.046692309596965104
deviation-center-line_min0.00493459316827017
deviation-heading_max0.17871954277822616
deviation-heading_mean0.12290238968183878
deviation-heading_median0.1404921816205833
deviation-heading_min0.043767648089746966
driven_any_max1.12
driven_any_mean0.9520000000000014
driven_any_median1.0400000000000025
driven_any_min0.7599999999999976
driven_lanedir_max1.0891907711875215
driven_lanedir_mean0.7157737741226108
driven_lanedir_min0.00431907202387416
in-drivable-lane_max0.7999999999999999
in-drivable-lane_mean0.1733333333333333
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.0400000000000025, "driven_lanedir": 0.00431907202387416, "in-drivable-lane": 0.7999999999999999, "deviation-heading": 0.1516782451032673, "deviation-center-line": 0.00493459316827017}, "ep001": {"driven_any": 0.760000000000006, "driven_lanedir": 0.7583624342945385, "in-drivable-lane": 0, "deviation-heading": 0.043767648089746966, "deviation-center-line": 0.05856940306194948}, "ep002": {"driven_any": 0.7599999999999976, "driven_lanedir": 0.6711343097180644, "in-drivable-lane": 0.033333333333333326, "deviation-heading": 0.17871954277822616, "deviation-center-line": 0.02298492709392346}, "ep003": {"driven_any": 1.0800000000000007, "driven_lanedir": 1.0558622833890554, "in-drivable-lane": 0.033333333333333326, "deviation-heading": 0.09985433081737012, "deviation-center-line": 0.0848318451919554}, "ep004": {"driven_any": 1.12, "driven_lanedir": 1.0891907711875215, "in-drivable-lane": 0, "deviation-heading": 0.1404921816205833, "deviation-center-line": 0.062140779468727016}}
140471261gunshiROS-based Lane Followingaido1_LF1_r3-v3step3-videossuccessyes3740:01:01(hidden)
other stats
videos1
140431261gunshiROS-based Lane Followingaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:22(hidden)
survival_time_median1.2000000000000004


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.01000800446048379, "good_angle": 1.686940448785267, "survival_time": 16.666666666666654, "traveled_tiles": 10, "valid_direction": 3.0666666666666824}, "ep001": {"nsteps": 31, "reward": -31.932894427689813, "good_angle": 0.15878295088032096, "survival_time": 1.0333333333333332, "traveled_tiles": 2, "valid_direction": 0.1333333333333333}, "ep002": {"nsteps": 36, "reward": -27.52177647418446, "good_angle": 0.19414464148172828, "survival_time": 1.2000000000000004, "traveled_tiles": 2, "valid_direction": 0.16666666666666718}, "ep003": {"nsteps": 36, "reward": -27.51240936987516, "good_angle": 0.04226222189540989, "survival_time": 1.2000000000000004, "traveled_tiles": 1, "valid_direction": 0.1333333333333334}, "ep004": {"nsteps": 14, "reward": -71.26851574437958, "good_angle": 0.1232290598985942, "survival_time": 0.4666666666666666, "traveled_tiles": 2, "valid_direction": 0.23333333333333328}}
good_angle_max1.686940448785267
good_angle_mean0.4410718645882641
good_angle_median0.15878295088032096
good_angle_min0.04226222189540989
reward_max0.01000800446048379
reward_mean-31.64511760233371
reward_median-27.52177647418446
reward_min-71.26851574437958
survival_time_max16.666666666666654
survival_time_mean4.113333333333331
survival_time_min0.4666666666666666
traveled_tiles_max10
traveled_tiles_mean3.4
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max3.0666666666666824
valid_direction_mean0.7466666666666699
valid_direction_median0.16666666666666718
valid_direction_min0.1333333333333333
14033494heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step1-simulationsuccessyes3740:03:28(hidden)
other stats
simulation-passed1
140311005sodabetastay youngaido1_LF1_r3-v3step1-simulationerroryes3740:00:32
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
140251259gunshiROS-based Lane Followingaido1_LF1_r3-v3step4-vizsuccessyes3740:01:49(hidden)
driven_lanedir_median0.3733605707851573
deviation-center-line_median0.11205683725851356
in-drivable-lane_median0


other stats
deviation-center-line_max2.2204416831349185
deviation-center-line_mean0.5150181221825256
deviation-center-line_min0.029027559570515123
deviation-heading_max2.6404688642993044
deviation-heading_mean0.7001698248895601
deviation-heading_median0.2288230430331044
deviation-heading_min0.17438574133268608
driven_any_max5.334922778206215
driven_any_mean1.3089926729328347
driven_any_median0.38545066654475296
driven_any_min0.12838745153826045
driven_lanedir_max4.603791717493052
driven_lanedir_mean1.150649359742452
driven_lanedir_min0.098001741272427
in-drivable-lane_max1.7000000000000144
in-drivable-lane_mean0.34000000000000286
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 5.334922778206215, "driven_lanedir": 4.603791717493052, "in-drivable-lane": 1.7000000000000144, "deviation-heading": 2.6404688642993044, "deviation-center-line": 2.2204416831349185}, "ep001": {"driven_any": 0.299884781051619, "driven_lanedir": 0.288234830511493, "in-drivable-lane": 0, "deviation-heading": 0.19563368035246295, "deviation-center-line": 0.07232540079129789}, "ep002": {"driven_any": 0.12838745153826045, "driven_lanedir": 0.098001741272427, "in-drivable-lane": 0, "deviation-heading": 0.26153779543024264, "deviation-center-line": 0.029027559570515123}, "ep003": {"driven_any": 0.38545066654475296, "driven_lanedir": 0.3733605707851573, "in-drivable-lane": 0, "deviation-heading": 0.2288230430331044, "deviation-center-line": 0.11205683725851356}, "ep004": {"driven_any": 0.3963176873233268, "driven_lanedir": 0.38985793865013174, "in-drivable-lane": 0, "deviation-heading": 0.17438574133268608, "deviation-center-line": 0.14123913015738354}}
14022492heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step1-simulationfailedyes3740:00:37
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 93, in run
    solve(params, cis)
  File "solution.py", line 33, in solve
    env = ScaleObservations(env)
  File "/workspace/wrappers.py", line 65, in __init__
    super().__init__(env)
TypeError: super() takes at least 1 argument (0 given)


(hidden)
140171259gunshiROS-based Lane Followingaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:22(hidden)
survival_time_median1.2666666666666673


other stats
episodes
details{"ep000": {"nsteps": 500, "reward": 0.4676013025026768, "good_angle": 1.1836417594264372, "survival_time": 16.666666666666654, "traveled_tiles": 9, "valid_direction": 2.733333333333351}, "ep001": {"nsteps": 30, "reward": -32.96535641414424, "good_angle": 0.15904109064589023, "survival_time": 1, "traveled_tiles": 2, "valid_direction": 0.16666666666666663}, "ep002": {"nsteps": 14, "reward": -71.2591038298394, "good_angle": 0.3024398451890282, "survival_time": 0.4666666666666666, "traveled_tiles": 1, "valid_direction": 0.3333333333333333}, "ep003": {"nsteps": 38, "reward": -26.028279338032007, "good_angle": 0.13938384435405965, "survival_time": 1.2666666666666673, "traveled_tiles": 1, "valid_direction": 0.23333333333333375}, "ep004": {"nsteps": 39, "reward": -25.45652350076498, "good_angle": 0.08948506492387784, "survival_time": 1.3000000000000007, "traveled_tiles": 2, "valid_direction": 0.10000000000000032}}
good_angle_max1.1836417594264372
good_angle_mean0.37479832090785864
good_angle_median0.15904109064589023
good_angle_min0.08948506492387784
reward_max0.4676013025026768
reward_mean-31.04833235605559
reward_median-26.028279338032007
reward_min-71.2591038298394
survival_time_max16.666666666666654
survival_time_mean4.139999999999997
survival_time_min0.4666666666666666
traveled_tiles_max9
traveled_tiles_mean3
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max2.733333333333351
valid_direction_mean0.713333333333337
valid_direction_median0.23333333333333375
valid_direction_min0.10000000000000032
14006745heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step3-videossuccessyes3740:01:53(hidden)
other stats
videos1
14003745heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median5.266666666666658


other stats
episodes
details{"ep000": {"nsteps": 158, "reward": -6.443896666687878, "good_angle": 0.5558670749976514, "survival_time": 5.266666666666658, "traveled_tiles": 8, "valid_direction": 1.733333333333335}, "ep001": {"nsteps": 89, "reward": -10.729929503095285, "good_angle": 0.10515861578190572, "survival_time": 2.966666666666666, "traveled_tiles": 5, "valid_direction": 0.30000000000000093}, "ep002": {"nsteps": 186, "reward": -4.875641627297286, "good_angle": 0.22887121095168503, "survival_time": 6.199999999999988, "traveled_tiles": 10, "valid_direction": 0.7333333333333318}, "ep003": {"nsteps": 173, "reward": -5.27640387682901, "good_angle": 0.21153864216163695, "survival_time": 5.766666666666656, "traveled_tiles": 8, "valid_direction": 0.6666666666666664}, "ep004": {"nsteps": 64, "reward": -15.04919581123977, "good_angle": 0.5091537933886537, "survival_time": 2.1333333333333355, "traveled_tiles": 2, "valid_direction": 0.7666666666666679}}
good_angle_max0.5558670749976514
good_angle_mean0.3221178674563066
good_angle_median0.22887121095168503
good_angle_min0.10515861578190572
reward_max-4.875641627297286
reward_mean-8.475013497029845
reward_median-6.443896666687878
reward_min-15.04919581123977
survival_time_max6.199999999999988
survival_time_mean4.466666666666661
survival_time_min2.1333333333333355
traveled_tiles_max10
traveled_tiles_mean6.6
traveled_tiles_median8
traveled_tiles_min2
valid_direction_max1.733333333333335
valid_direction_mean0.8400000000000004
valid_direction_median0.7333333333333318
valid_direction_min0.30000000000000093
13993488JonJP pipelineaido1_LF1_r3-v3step4-vizsuccessyes3740:02:13(hidden)
driven_lanedir_median0.42227261567306407
deviation-center-line_median0.09452149957254032
in-drivable-lane_median1.1999999999999995


other stats
deviation-center-line_max0.509135173436567
deviation-center-line_mean0.17499721258171877
deviation-center-line_min0.05977368650864371
deviation-heading_max10.54133142278161
deviation-heading_mean2.445508858270102
deviation-heading_median0.43623171255341986
deviation-heading_min0.39555103215797793
driven_any_max5.304125776697546
driven_any_mean1.656727145077686
driven_any_median0.8905392926537553
driven_any_min0.1965478520622135
driven_lanedir_max0.5814875869893381
driven_lanedir_mean0.3307504517839068
driven_lanedir_min0.0029119701673430143
in-drivable-lane_max14.599999999999952
in-drivable-lane_mean5.8533333333333175
in-drivable-lane_min0.36666666666666664
per-episodes
details{"ep000": {"driven_any": 0.1965478520622135, "driven_lanedir": 0.0029119701673430143, "in-drivable-lane": 0.36666666666666664, "deviation-heading": 10.54133142278161, "deviation-center-line": 0.509135173436567}, "ep001": {"driven_any": 1.0430997801399369, "driven_lanedir": 0.5814875869893381, "in-drivable-lane": 1.1999999999999995, "deviation-heading": 0.39555103215797793, "deviation-center-line": 0.13026589242752282}, "ep002": {"driven_any": 5.304125776697546, "driven_lanedir": 0.19574528279350775, "in-drivable-lane": 11.966666666666631, "deviation-heading": 0.43623171255341986, "deviation-center-line": 0.05977368650864371}, "ep003": {"driven_any": 0.8493230238349783, "driven_lanedir": 0.42227261567306407, "in-drivable-lane": 1.1333333333333355, "deviation-heading": 0.3988180652546159, "deviation-center-line": 0.08128981096332001}, "ep004": {"driven_any": 0.8905392926537553, "driven_lanedir": 0.45133480329628095, "in-drivable-lane": 14.599999999999952, "deviation-heading": 0.4556120586028843, "deviation-center-line": 0.09452149957254032}}
139791255gunshiROS-based Lane Followingaido1_LF1_r3-v3step3-videossuccessyes3740:00:59(hidden)
other stats
videos1
13976487happyduckieROS-based Lane Followingaido1_LF1_r3-v3step4-vizsuccessyes3740:02:49(hidden)
driven_lanedir_median0.3880610979918855
deviation-center-line_median0.13664925000771344
in-drivable-lane_median0


other stats
deviation-center-line_max2.0343215338656777
deviation-center-line_mean0.8453138210483389
deviation-center-line_min0.07311490591334209
deviation-heading_max1.9797248911477023
deviation-heading_mean0.8015501067684795
deviation-heading_median0.2465135448335628
deviation-heading_min0.17384115244657822
driven_any_max5.335459318743161
driven_any_mean2.3353841958378245
driven_any_median0.396353136018631
driven_any_min0.2890563026297844
driven_lanedir_max3.6746625476582215
driven_lanedir_mean1.561467966843581
driven_lanedir_min0.27676157363072607
in-drivable-lane_max6.466666666666647
in-drivable-lane_mean2.2333333333333263
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 5.335459318743161, "driven_lanedir": 3.1633806569128593, "in-drivable-lane": 6.466666666666647, "deviation-heading": 1.3769178031616027, "deviation-center-line": 1.897159035212376}, "ep001": {"driven_any": 0.3212814612457478, "driven_lanedir": 0.30447395802421173, "in-drivable-lane": 0, "deviation-heading": 0.2465135448335628, "deviation-center-line": 0.08532438024258489}, "ep002": {"driven_any": 0.2890563026297844, "driven_lanedir": 0.27676157363072607, "in-drivable-lane": 0, "deviation-heading": 0.23075314225295135, "deviation-center-line": 0.07311490591334209}, "ep003": {"driven_any": 5.334770760551796, "driven_lanedir": 3.6746625476582215, "in-drivable-lane": 4.699999999999983, "deviation-heading": 1.9797248911477023, "deviation-center-line": 2.0343215338656777}, "ep004": {"driven_any": 0.396353136018631, "driven_lanedir": 0.3880610979918855, "in-drivable-lane": 0, "deviation-heading": 0.17384115244657822, "deviation-center-line": 0.13664925000771344}}
13968487happyduckieROS-based Lane Followingaido1_LF1_r3-v3step3-videossuccessyes3740:01:28(hidden)
other stats
videos1
13955487happyduckieROS-based Lane Followingaido1_LF1_r3-v3step1-simulationsuccessyes3740:06:41(hidden)
other stats
simulation-passed1
13951998lmandrileTensorflow templateaido1_LF1_r3-v3step1-simulationfailedyes3740:00:44
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/notebooks/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 86, in run
    solve(params, cis)  # let's try to solve the challenge,
  File "solution.py", line 29, in solve
    from model import TfInference
  File "/workspace/model.py", line 5, in <module>
    from _layers import one_residual
ImportError: No module named _layers


(hidden)
13932740Andrea CensiCopy of #15: sub 574 by trimcao (PyTorch template)aido1_LF1_r3-v3step1-simulationsuccessyes3740:03:30(hidden)
other stats
simulation-passed1
139271249gunshiROS-based Lane Followingaido1_LF1_r3-v3step4-vizsuccessyes3740:01:54(hidden)
driven_lanedir_median0.45642259386947104
deviation-center-line_median0.1660657951599833
in-drivable-lane_median0.8999999999999968


other stats
deviation-center-line_max0.9107384421667792
deviation-center-line_mean0.2983459954507724
deviation-center-line_min0
deviation-heading_max7.0152968568589404
deviation-heading_mean2.2493375241439986
deviation-heading_median1.2177115986541922
deviation-heading_min0
driven_any_max5.328808005418226
driven_any_mean1.667358632935457
driven_any_median0.963363970852138
driven_any_min0.4392304877641521
driven_lanedir_max2.1065949074005244
driven_lanedir_mean0.6591226152486653
driven_lanedir_min0
in-drivable-lane_max6.966666666666647
in-drivable-lane_mean2.1599999999999957
in-drivable-lane_min0.6999999999999985
per-episodes
details{"ep000": {"driven_any": 0.4392304877641521, "driven_lanedir": 0, "in-drivable-lane": 1.400000000000001, "deviation-heading": 0, "deviation-center-line": 0}, "ep001": {"driven_any": 0.963363970852138, "driven_lanedir": 0.5287714283897977, "in-drivable-lane": 0.8999999999999968, "deviation-heading": 1.2177115986541922, "deviation-center-line": 0.11712290838698544}, "ep002": {"driven_any": 0.610168392895075, "driven_lanedir": 0.20382414658353376, "in-drivable-lane": 0.8333333333333356, "deviation-heading": 0.9273991669318156, "deviation-center-line": 0.1660657951599833}, "ep003": {"driven_any": 5.328808005418226, "driven_lanedir": 2.1065949074005244, "in-drivable-lane": 6.966666666666647, "deviation-heading": 7.0152968568589404, "deviation-center-line": 0.9107384421667792}, "ep004": {"driven_any": 0.9952223077476932, "driven_lanedir": 0.45642259386947104, "in-drivable-lane": 0.6999999999999985, "deviation-heading": 2.086279998275045, "deviation-center-line": 0.29780283154011383}}
139241249gunshiROS-based Lane Followingaido1_LF1_r3-v3step3-videossuccessyes3740:01:27(hidden)
other stats
videos1
139201249gunshiROS-based Lane Followingaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:21(hidden)
survival_time_median3.0666666666666655


other stats
episodes
details{"ep000": {"nsteps": 43, "reward": -24.665131991685826, "good_angle": 0.8359945079864171, "survival_time": 1.4333333333333345, "traveled_tiles": 1, "valid_direction": 0.6333333333333345}, "ep001": {"nsteps": 92, "reward": -10.983419067934966, "good_angle": 1.7104272632766386, "survival_time": 3.0666666666666655, "traveled_tiles": 2, "valid_direction": 2.0333333333333323}, "ep002": {"nsteps": 59, "reward": -17.733325373523442, "good_angle": 2.297130295366154, "survival_time": 1.9666666666666697, "traveled_tiles": 1, "valid_direction": 1.7000000000000026}, "ep003": {"nsteps": 500, "reward": -0.3605966798245208, "good_angle": 20.817308456605904, "survival_time": 16.666666666666654, "traveled_tiles": 3, "valid_direction": 12.4}, "ep004": {"nsteps": 95, "reward": -10.748102120511035, "good_angle": 3.433560232240978, "survival_time": 3.166666666666665, "traveled_tiles": 2, "valid_direction": 2.766666666666665}}
good_angle_max20.817308456605904
good_angle_mean5.818884151095219
good_angle_median2.297130295366154
good_angle_min0.8359945079864171
reward_max-0.3605966798245208
reward_mean-12.898115046695958
reward_median-10.983419067934966
reward_min-24.665131991685826
survival_time_max16.666666666666654
survival_time_mean5.259999999999997
survival_time_min1.4333333333333345
traveled_tiles_max3
traveled_tiles_mean1.8
traveled_tiles_median2
traveled_tiles_min1
valid_direction_max12.4
valid_direction_mean3.906666666666667
valid_direction_median2.0333333333333323
valid_direction_min0.6333333333333345
139091248yun chenPyTorch DDPG templateaido1_LF1_r3-v3step1-simulationsuccessyes3740:06:51(hidden)
other stats
simulation-passed1
13905479JonJP pipelineaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median6.666666666666653


other stats
episodes
details{"ep000": {"nsteps": 105, "reward": -10.621635886033376, "good_angle": 4.36521953282019, "survival_time": 3.4999999999999973, "traveled_tiles": 1, "valid_direction": 2.1333333333333298}, "ep001": {"nsteps": 53, "reward": -19.249537127338492, "good_angle": 1.2916700360980615, "survival_time": 1.7666666666666688, "traveled_tiles": 2, "valid_direction": 1.5666666666666689}, "ep002": {"nsteps": 239, "reward": -4.468814524838011, "good_angle": 10.18516514293818, "survival_time": 7.966666666666648, "traveled_tiles": 1, "valid_direction": 7.866666666666648}, "ep003": {"nsteps": 267, "reward": -4.08671248723772, "good_angle": 13.66818823417476, "survival_time": 8.899999999999979, "traveled_tiles": 1, "valid_direction": 8.533333333333312}, "ep004": {"nsteps": 200, "reward": -6.732754115583375, "good_angle": 4.742441215110017, "survival_time": 6.666666666666653, "traveled_tiles": 2, "valid_direction": 6.099999999999985}}
good_angle_max13.66818823417476
good_angle_mean6.850536832228242
good_angle_median4.742441215110017
good_angle_min1.2916700360980615
reward_max-4.08671248723772
reward_mean-9.031890828206194
reward_median-6.732754115583375
reward_min-19.249537127338492
survival_time_max8.899999999999979
survival_time_mean5.759999999999989
survival_time_min1.7666666666666688
traveled_tiles_max2
traveled_tiles_mean1.4
traveled_tiles_median1
traveled_tiles_min1
valid_direction_max8.533333333333312
valid_direction_mean5.2399999999999896
valid_direction_median6.099999999999985
valid_direction_min1.5666666666666689
13895479JonJP pipelineaido1_LF1_r3-v3step1-simulationsuccessyes3740:04:25(hidden)
other stats
simulation-passed1
138861246DavidPytorch ILaido1_LF1_r3-v3step1-simulationsuccessyes3740:03:42(hidden)
other stats
simulation-passed1
13877988placailleDolores' Awakeningaido1_LF1_r3-v3step3-videossuccessyes3740:01:14(hidden)
other stats
videos1
13872988placailleDolores' Awakeningaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:24(hidden)
survival_time_median4.233333333333328


other stats
episodes
details{"ep000": {"nsteps": 206, "reward": -4.506861481404167, "good_angle": 1.7774891512576485, "survival_time": 6.866666666666652, "traveled_tiles": 10, "valid_direction": 2.0999999999999948}, "ep001": {"nsteps": 127, "reward": -7.241945376751695, "good_angle": 0.48253168957222087, "survival_time": 4.233333333333328, "traveled_tiles": 8, "valid_direction": 0.7333333333333325}, "ep002": {"nsteps": 129, "reward": -7.075629496759222, "good_angle": 0.7947785814601735, "survival_time": 4.2999999999999945, "traveled_tiles": 5, "valid_direction": 1.033333333333332}, "ep003": {"nsteps": 55, "reward": -17.891810695759276, "good_angle": 0.12712728786850502, "survival_time": 1.8333333333333357, "traveled_tiles": 3, "valid_direction": 0.3000000000000006}, "ep004": {"nsteps": 78, "reward": -12.289562796935057, "good_angle": 0.6858427053202054, "survival_time": 2.6000000000000005, "traveled_tiles": 4, "valid_direction": 1.1999999999999986}}
good_angle_max1.7774891512576485
good_angle_mean0.7735538830957507
good_angle_median0.6858427053202054
good_angle_min0.12712728786850502
reward_max-4.506861481404167
reward_mean-9.801161969521884
reward_median-7.241945376751695
reward_min-17.891810695759276
survival_time_max6.866666666666652
survival_time_mean3.9666666666666623
survival_time_min1.8333333333333357
traveled_tiles_max10
traveled_tiles_mean6
traveled_tiles_median5
traveled_tiles_min3
valid_direction_max2.0999999999999948
valid_direction_mean1.0733333333333317
valid_direction_median1.033333333333332
valid_direction_min0.3000000000000006
13869476JonJP pipelineaido1_LF1_r3-v3step3-videossuccessyes3740:00:42(hidden)
other stats
videos1
13858987placailleDolores' Awakeningaido1_LF1_r3-v3step3-videossuccessyes3740:01:19(hidden)
other stats
videos1
13856987placailleDolores' Awakeningaido1_LF1_r3-v3step2-scoringsuccessyes3740:00:22(hidden)
survival_time_median2.6000000000000005


other stats
episodes
details{"ep000": {"nsteps": 230, "reward": -4.091312414674979, "good_angle": 5.238277985412388, "survival_time": 7.666666666666649, "traveled_tiles": 7, "valid_direction": 3.933333333333324}, "ep001": {"nsteps": 21, "reward": -47.27952035410063, "good_angle": 0.0030213493876724944, "survival_time": 0.7, "traveled_tiles": 3, "valid_direction": 0}, "ep002": {"nsteps": 393, "reward": -1.9488024320879969, "good_angle": 1.5674971887310316, "survival_time": 13.099999999999964, "traveled_tiles": 18, "valid_direction": 2.6333333333333258}, "ep003": {"nsteps": 55, "reward": -17.891810695759276, "good_angle": 0.12712728786850502, "survival_time": 1.8333333333333357, "traveled_tiles": 3, "valid_direction": 0.3000000000000006}, "ep004": {"nsteps": 78, "reward": -12.4009771677546, "good_angle": 0.7771205766834783, "survival_time": 2.6000000000000005, "traveled_tiles": 4, "valid_direction": 1.266666666666665}}
good_angle_max5.238277985412388
good_angle_mean1.542608877616615
good_angle_median0.7771205766834783
good_angle_min0.0030213493876724944
reward_max-1.9488024320879969
reward_mean-16.722484612875498
reward_median-12.4009771677546
reward_min-47.27952035410063
survival_time_max13.099999999999964
survival_time_mean5.17999999999999
survival_time_min0.7
traveled_tiles_max18
traveled_tiles_mean7
traveled_tiles_median4
traveled_tiles_min3
valid_direction_max3.933333333333324
valid_direction_mean1.626666666666663
valid_direction_median1.266666666666665
valid_direction_min0
138461241gunshiROS-based Lane Followingaido1_LF1_r3-v3step4-vizsuccessyes3740:03:08(hidden)
driven_lanedir_median1.0112446686487098
deviation-center-line_median0.5833575250537845
in-drivable-lane_median3.099999999999995


other stats
deviation-center-line_max0.9939573406673812
deviation-center-line_mean0.6441400763869072
deviation-center-line_min0.39639642613395376
deviation-heading_max7.762271591345482
deviation-heading_mean4.97012624849542
deviation-heading_median4.678510639850773
deviation-heading_min3.216738739883372
driven_any_max5.327394609415363
driven_any_mean3.342444431330879
driven_any_median2.994453145484006
driven_any_min2.086265898870574
driven_lanedir_max2.386158297414386
driven_lanedir_mean1.470119970946197
driven_lanedir_min0.8153610055228977
in-drivable-lane_max5.7333333333333245
in-drivable-lane_mean3.626666666666659
in-drivable-lane_min2.499999999999994
per-episodes
details{"ep000": {"driven_any": 2.387264909048229, "driven_lanedir": 1.00016952983618, "in-drivable-lane": 2.8999999999999937, "deviation-heading": 3.237413419670277, "deviation-center-line": 0.5833575250537845}, "ep001": {"driven_any": 5.327394609415363, "driven_lanedir": 2.386158297414386, "in-drivable-lane": 5.7333333333333245, "deviation-heading": 7.762271591345482, "deviation-center-line": 0.9939573406673812}, "ep002": {"driven_any": 2.994453145484006, "driven_lanedir": 1.0112446686487098, "in-drivable-lane": 3.899999999999988, "deviation-heading": 4.678510639850773, "deviation-center-line": 0.43452943926472265}, "ep003": {"driven_any": 3.9168435938362234, "driven_lanedir": 2.1376663533088105, "in-drivable-lane": 3.099999999999995, "deviation-heading": 5.9556968517272, "deviation-center-line": 0.812459650814694}, "ep004": {"driven_any": 2.086265898870574, "driven_lanedir": 0.8153610055228977, "in-drivable-lane": 2.499999999999994, "deviation-heading": 3.216738739883372, "deviation-center-line": 0.39639642613395376}}
13835729heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step4-vizsuccessyes3740:03:39(hidden)
driven_lanedir_median2.9719025309559286
deviation-center-line_median1.2828172855158575
in-drivable-lane_median0.6999999999999982


other stats
deviation-center-line_max1.3268156399619444
deviation-center-line_mean0.8158175883315473
deviation-center-line_min0
deviation-heading_max12.30286214726098
deviation-heading_mean7.492911373676364
deviation-heading_median11.791788515571948
deviation-heading_min0
driven_any_max4.639692202041085
driven_any_mean2.9515150393707104
driven_any_median4.639692202041062
driven_any_min0.232916275202866
driven_lanedir_max2.97366098162551
driven_lanedir_mean1.8585194438277488
driven_lanedir_min0
in-drivable-lane_max0.8666666666666666
in-drivable-lane_mean0.5799999999999993
in-drivable-lane_min0.16666666666666685
per-episodes
details{"ep000": {"driven_any": 0.232916275202866, "driven_lanedir": 0, "in-drivable-lane": 0.8666666666666666, "deviation-heading": 0, "deviation-center-line": 0}, "ep001": {"driven_any": 4.639692202041062, "driven_lanedir": 2.973359424269386, "in-drivable-lane": 0.7333333333333325, "deviation-heading": 11.791788515571948, "deviation-center-line": 1.299074331276045}, "ep002": {"driven_any": 0.6055823155274532, "driven_lanedir": 0.3736742822879182, "in-drivable-lane": 0.16666666666666685, "deviation-heading": 1.5163173734972024, "deviation-center-line": 0.17038068490388883}, "ep003": {"driven_any": 4.6396922020410845, "driven_lanedir": 2.97366098162551, "in-drivable-lane": 0.6999999999999982, "deviation-heading": 11.853588832051686, "deviation-center-line": 1.2828172855158575}, "ep004": {"driven_any": 4.639692202041085, "driven_lanedir": 2.9719025309559286, "in-drivable-lane": 0.43333333333333224, "deviation-heading": 12.30286214726098, "deviation-center-line": 1.3268156399619444}}
13829730heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step1-simulationsuccessyes3740:05:38(hidden)
other stats
simulation-passed1
138111240heyt0nySAIC MOSCOW MMLaido1_LF1_r3-v3step1-simulationfailedyes3740:12:40
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 477, in wrap_evaluator
    cie.wait_for_solution()
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 270, in wait_for_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Time out: Timeout of 600 while waiting for /challenge-solution-output/output-solution.yaml.
(hidden)
13807728heyt0nyAI DL RL MML XXXL 2k18 yooaido1_LF1_r3-v3step1-simulationfailedyes3740:00:38
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 486, in wrap_evaluator
    raise InvalidSubmission(out[SPECIAL_INVALID_SUBMISSION])
InvalidSubmission: Invalid solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 590, in wrap_solution
    raise InvalidSubmission(msg)
InvalidSubmission: Uncaught exception in solution:
Traceback (most recent call last):
  File "/workspace/src/duckietown-challenges/src/duckietown_challenges/cie_concrete.py", line 585, in wrap_solution
    solution.run(cis)
  File "solution.py", line 116, in run
    solve(params, cis)
  File "solution.py", line 75, in solve
    cis.info("OBS", str(observation.shape), str(observation.min()), str(observation.max()))
TypeError: info() takes exactly 2 arguments (5 given)


(hidden)
13805472JonJP pipelineaido1_LF1_r3-v3step1-simulationerroryes3740:00:42
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
137911236gunshiROS-based Lane Followingaido1_LF1_r3-v3step3-videossuccessyes3740:01:26(hidden)
other stats
videos1
137801237gunshiROS-based Lane Followingaido1_LF1_r3-v3step1-simulationsuccessyes3740:03:42(hidden)
other stats
simulation-passed1
13777469BenjaminPyTorch DDPG templateaido1_LF1_r3-v3step1-simulationerroryes3740:00:30
The result file is n [...]
The result file is not found. This usually means that the evaluator did not finish
and some times that there was an import error.

Check the evaluator log to see what happened.
(hidden)
13771723BenjaminMy modified ROS-based Lane Followingaido1_LF1_r3-v3step4-vizsuccessyes3740:00:59(hidden)
driven_lanedir_median1.2198948596118937
deviation-center-line_median0.0647549361485265
in-drivable-lane_median0


other stats
deviation-center-line_max0.12250993205105008
deviation-center-line_mean0.08418384090727651
deviation-center-line_min0.053935263197224144
deviation-heading_max0.538069852110779
deviation-heading_mean0.2624569748485178
deviation-heading_median0.292835403175886
deviation-heading_min0.043767648089746966
driven_any_max1.8296297320528372
driven_any_mean1.16857594110514
driven_any_median1.273332933336607
driven_any_min0.468905615580903
driven_lanedir_max1.5635562895933086
driven_lanedir_mean1.0904869554379963
driven_lanedir_min0.4644103869546478
in-drivable-lane_max0.3333333333333333
in-drivable-lane_mean0.06666666666666667
in-drivable-lane_min0
per-episodes
details{"ep000": {"driven_any": 1.8296297320528372, "driven_lanedir": 1.5635562895933086, "in-drivable-lane": 0.3333333333333333, "deviation-heading": 0.538069852110779, "deviation-center-line": 0.12250993205105008}, "ep001": {"driven_any": 0.760000000000006, "driven_lanedir": 0.7583624342945385, "in-drivable-lane": 0, "deviation-heading": 0.043767648089746966, "deviation-center-line": 0.05856940306194948}, "ep002": {"driven_any": 1.273332933336607, "driven_lanedir": 1.2198948596118937, "in-drivable-lane": 0, "deviation-heading": 0.292835403175886, "deviation-center-line": 0.0647549361485265}, "ep003": {"driven_any": 0.468905615580903, "driven_lanedir": 0.4644103869546478, "in-drivable-lane": 0, "deviation-heading": 0.04624009764223321, "deviation-center-line": 0.053935263197224144}, "ep004": {"driven_any": 1.5110114245553468, "driven_lanedir": 1.4462108067355923, "in-drivable-lane": 0, "deviation-heading": 0.3913718732239438, "deviation-center-line": 0.1211496700776323}}