Submission 823

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 117, in run
    solve(params, cis)
  File "solution.py", line 64, in solve
    model = Model(config_name=config_name, config=config)
  File "/workspace/model.py", line 15, in __init__
    self.init_model(config_name)
  File "/workspace/model.py", line 45, in init_model
    map_location=self.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 RainbowDQN:
	Missing key(s) in state_dict: "noisy_value1.bias_sigma", "noisy_value1.weight_epsilon", "noisy_value1.weight_mu", "noisy_value1.weight_sigma", "noisy_value1.bias_mu", "noisy_value1.bias_epsilon", "noisy_value2.bias_sigma", "noisy_value2.weight_epsilon", "noisy_value2.weight_mu", "noisy_value2.weight_sigma", "noisy_value2.bias_mu", "noisy_value2.bias_epsilon", "noisy_advantage1.bias_sigma", "noisy_advantage1.weight_epsilon", "noisy_advantage1.weight_mu", "noisy_advantage1.weight_sigma", "noisy_advantage1.bias_mu", "noisy_advantage1.bias_epsilon", "noisy_advantage2.bias_sigma", "noisy_advantage2.weight_epsilon", "noisy_advantage2.weight_mu", "noisy_advantage2.weight_sigma", "noisy_advantage2.bias_mu", "noisy_advantage2.bias_epsilon". 
	Unexpected key(s) in state_dict: "noisy2.weight_mu", "noisy2.weight_sigma", "noisy2.bias_mu", "noisy2.bias_sigma", "noisy2.weight_epsilon", "noisy2.bias_epsilon", "noisy1.weight_mu", "noisy1.weight_sigma", "noisy1.bias_mu", "noisy1.bias_sigma", "noisy1.weight_epsilon", "noisy1.bias_epsilon".