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ghliu / pytorch-ddpg

Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch
Apache License 2.0
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NotImplementedError #13

Open ashing-zhang opened 2 years ago

ashing-zhang commented 2 years ago

Traceback (most recent call last): File "D:\Master\Codes\pytorch-ddpg\main.py", line 156, in train(args.train_iter, agent, env, evaluate, File "D:\Master\Codes\pytorch-ddpg\main.py", line 44, in train observation2, reward, done, info = env.step(action) File "D:\AI\Software\Conda\Miniconda\envs\torch\lib\site-packages\gym\core.py", line 349, in step return self.env.step(self.action(action)) File "D:\AI\Software\Conda\Miniconda\envs\torch\lib\site-packages\gym\core.py", line 353, in action raise NotImplementedError NotImplementedError

fuhaiwang commented 2 years ago

You can change the code line#126 in main.py: from env = NormalizedEnv(gym.make(args.env)) to env = gym.make(args.env)

Or you can do: change the code line#8 and line#13 in normalized_env.py: from def _action(self, action): and def _reverse_action(self, action): to def action(self, action): and def reverse_action(self, action):

Because the gym is updated (Maybe?). The function in class NormalizedEnv should delete "_". You can see the father class ActionWrapper in https://github.com/openai/gym/blob/master/gym/core.py

That's all!

ashing-zhang commented 2 years ago

Thank you sir.

---Original--- From: @.> Date: Fri, Nov 26, 2021 17:45 PM To: @.>; Cc: @.**@.>; Subject: Re: [ghliu/pytorch-ddpg] NotImplementedError (Issue #13)

You can change the code line#126 in main.py: from env = NormalizedEnv(gym.make(args.env)) to env = gym.make(args.env)

Or you can do: change the code line#8 and line#13 in normalized_env.py: from def _action(self, action): and def _reverse_action(self, action): to def action(self, action): and def reverse_action(self, action):

Because the gym is updated (Maybe?). The function in class NormalizedEnv should delete "_". You can see the father class ActionWrapper in https://github.com/openai/gym/blob/master/gym/core.py

That's all!

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