cgel / drl Goto Github PK
View Code? Open in Web Editor NEWA collection of Deep Reinforcement Learning algorithms implemented in tensorflow. Very extensible. High performing DQN implementation.
A collection of Deep Reinforcement Learning algorithms implemented in tensorflow. Very extensible. High performing DQN implementation.
In your code RM.add(state, action, reward, terminal) in BaseAgent, timestep of self.game_state[:, :, :, -1] self.game_action self.game_reward seem not consistent. your self.game_reward is not the reward when you take action self.game_action on self.game_state[:,:,;-1], however, it is the reward you got from last step.
Hey @cgel,
I have been studying your code since you posted in dennybritz repo and it has been really helpful!!
I have one comment regarding the Replay Memory Class. If I am not wrong, you use the following line to avoid sampling terminal states from the RM:
if self.terminals[(index - self.buff_size -1):index].any():
continue
But I would say that the conditional should be more like:
if self.terminals[(index - self.buff_size +1):index+1].any():
So that you verify that none of the last buff_size frames are terminal. Ideally, however, there should be another logic block taking care of verifying that index is not smaller than buff_size, and if it is, check also the last entries of the RM (similar to what it's done in the function get_state).
I hope I was clear XD. Thanks again for sharing the code, it helped me a lot.
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