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Traceback (most recent call last): [665/3388]
File "/home/xhwang/anaconda3/envs/pymarl/lib/python3.8/site-packages/sacred/experiment.py", line 312, in run_commandline
return self.run(
File "/home/xhwang/anaconda3/envs/pymarl/lib/python3.8/site-packages/sacred/experiment.py", line 276, in run
run()
File "/home/xhwang/anaconda3/envs/pymarl/lib/python3.8/site-packages/sacred/run.py", line 238, in __call__
self.result = self.main_function(*args)
File "/home/xhwang/anaconda3/envs/pymarl/lib/python3.8/site-packages/sacred/config/captured_function.py", line 42, in captured_function
result = wrapped(*args, **kwargs)
File "src/main.py", line 38, in my_main
run_REGISTRY[_config['run']](_run, config, _log)
File "/NAS2020/Workspaces/DRLGroup/xhwang/Lab/SCII/pymarl2/src/run/run.py", line 54, in run
run_sequential(args=args, logger=logger)
File "/NAS2020/Workspaces/DRLGroup/xhwang/Lab/SCII/pymarl2/src/run/run.py", line 195, in run_sequential
learner.train(episode_sample, runner.t_env, episode)
File "/NAS2020/Workspaces/DRLGroup/xhwang/Lab/SCII/pymarl2/src/learners/policy_gradient_v2.py", line 58, in train
advantages, td_error, targets_taken, log_pi_taken, entropy = self._calculate_advs(batch, rewards, terminated, actions, avail_actions,
File "/NAS2020/Workspaces/DRLGroup/xhwang/Lab/SCII/pymarl2/src/learners/policy_gradient_v2.py", line 115, in _calculate_advs
entropy = categorical_entropy(pi).reshape(-1) #[bs, t, n_agents, 1]
File "/NAS2020/Workspaces/DRLGroup/xhwang/Lab/SCII/pymarl2/src/components/action_selectors.py", line 110, in categorical_entropy
return Categorical(probs=probs).entropy()
File "/home/xhwang/anaconda3/envs/pymarl/lib/python3.8/site-packages/torch/distributions/categorical.py", line 64, in __init__
super(Categorical, self).__init__(batch_shape, validate_args=validate_args)
File "/home/xhwang/anaconda3/envs/pymarl/lib/python3.8/site-packages/torch/distributions/distribution.py", line 55, in __init__
raise ValueError(
ValueError: Expected parameter probs (Tensor of shape (8, 54, 10, 18)) of distribution Categorical(probs: torch.Size([8, 54, 10, 18])) to satisfy the constrai
nt Simplex(), but found invalid values:
后面一截是数据没有贴上来,问题就是里面有nan
The text was updated successfully, but these errors were encountered:
后面一截是数据没有贴上来,问题就是里面有nan
The text was updated successfully, but these errors were encountered: