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decisionforce / CoPO

[NeurIPS 2021] Official implementation of paper "Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization".
Apache License 2.0
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AssertionError in visualizing trained models. #46

Closed gcruiser closed 8 months ago

gcruiser commented 8 months ago

Hi! Thanks for the excellent work! After I used train_all_copo_dist.py to train the model and try to visualize it, I found that the dimensions of OBS and Weights did not match, even the same problem in the . This problem occurred in every environment I trained, and the number of rows in OBS is always 1 less than the number of columns in Weights. But I can visualize the by the new_vis.py, which is wired.

Traceback (most recent call last):
  File "/home/CoPO_tf/CoPO/copo_code/copo/vis_from_checkpoint.py", line 91, in <module>
    action = policy_function(o, d)
  File "/home/CoPO_tf/CoPO/copo_code/copo/eval/get_policy_function.py", line 170, in __call__
    actions = self.policy(obs_to_be_eval)
  File "/home/CoPO_tf/CoPO/copo_code/copo/vis_from_checkpoint.py", line 72, in policy
    ret = policy_class(weights, obs, policy_name=policy_name, layer_name_suffix="_1", deterministic=deterministic)
  File "/home/CoPO_tf/CoPO/copo_code/copo/eval/get_policy_function.py", line 61, in _compute_actions_for_tf_policy
    assert obs.shape[1] == weights[s].shape[0], (obs.shape, weights[s].shape)
AssertionError: ((20, 96), (97, 256))