Comments (1)
I have the same question, since I've seen implementations that normalize these gradients. However, if batch size is always the same constant, then it doesn't matter that much and you can just adjust the learning rate, right?
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Related Issues (18)
- run env HumanoidStandup-v1 error HOT 1
- Bug with FilteredEnv
- error: python gym_ddpg.py
- the dimension of Input and Output
- fatal error
- Action used for gradient calculation HOT 2
- No target networks in the implementation HOT 1
- DDPG Actor output saturate
- Question regarding the calculation of the actor gradient. HOT 5
- how to save the actor and critic weights
- Error: No module named utlility HOT 2
- Issue about Segmentation fault (core dumped) HOT 1
- mistake found
- Actions generated by Actor network increases to 1. and stay there HOT 1
- X Error of failed request: BadRequest (invalid request code or no such operation)
- How to train DDPG agent on Reacher-v1 env?
- AttributeError: 'FilteredEnv' object has no attribute 'monitor' HOT 2
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