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If you use MultiDiscrete in Gym as the action space class, and use the mappo algorithm, an error will occur:
File "D:\anaconda3\envs\ray\lib\site-packages\marllib\marl\models\zoo\mlp\cc_mlp.py", line 125, in central_value_function
x = torch.cat([x.reshape(B, -1)] + opponent_actions_ls, 1)
RuntimeError: Tensors must have same number of dimensions: got 2 and 3
This is because the MultiDiscrete situation is not handled in def central_value_function(self, state, opponent_actions=None) of cc_mlp.py, resulting in different dimensions between opponent_actions_ls and [x.reshape(B, -1)].
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如果使用Gym的MultiDiscrete作为行动空间,同时使用MAPPO类的算法(用了cc_mlp.py的算法都会出现问题)将会出现报错: File "D:\anaconda3\envs\ray\lib\site-packages\marllib\marl\models\zoo\mlp\cc_mlp.py", line 125, in central_value_function
x = torch.cat([x.reshape(B, -1)] + opponent_actions_ls, 1)
RuntimeError: Tensors must have same number of dimensions: got 2 and 3
这是因为在cc_mlp.py文件里的def central_value_function(self, state, opponent_actions=None)函数中没有考虑MultiDiscrete作为动作空间的情况,从而使拼接时维数不兼容。请加上MultiDiscrete情况下opponent_actions_ls的计算结果。
The text was updated successfully, but these errors were encountered:
If you use
MultiDiscrete
inGym
as the action space class, and use the mappo algorithm, an error will occur:File "D:\anaconda3\envs\ray\lib\site-packages\marllib\marl\models\zoo\mlp\cc_mlp.py", line 125, in central_value_function
x = torch.cat([x.reshape(B, -1)] + opponent_actions_ls, 1)
RuntimeError: Tensors must have same number of dimensions: got 2 and 3
This is because the MultiDiscrete situation is not handled in def central_value_function(self, state, opponent_actions=None) of cc_mlp.py, resulting in different dimensions between opponent_actions_ls and [x.reshape(B, -1)].
======
如果使用Gym的MultiDiscrete作为行动空间,同时使用MAPPO类的算法(用了cc_mlp.py的算法都会出现问题)将会出现报错:
File "D:\anaconda3\envs\ray\lib\site-packages\marllib\marl\models\zoo\mlp\cc_mlp.py", line 125, in central_value_function
x = torch.cat([x.reshape(B, -1)] + opponent_actions_ls, 1)
RuntimeError: Tensors must have same number of dimensions: got 2 and 3
这是因为在cc_mlp.py文件里的def central_value_function(self, state, opponent_actions=None)函数中没有考虑MultiDiscrete作为动作空间的情况,从而使拼接时维数不兼容。请加上MultiDiscrete情况下opponent_actions_ls的计算结果。
The text was updated successfully, but these errors were encountered: