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Pytorch Implementation of Double DQN Algorithm

Python 100.00%
deep-reinforcement-learning pytorch gym deep-learning double-dqn

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pytorch-double-dqn's Issues

Get actions for next states by 'Qnet' or 'target_Qnet'?

Hello, thank you so much for sharing this code structure! I got one thing no very sure about in your code.

Pytorch-Double-DQN/agent.py

Lines 94 to 98 in 1cff44d

if self.args['double_dqn']:
_, next_state_actions = self.Qnet(non_final_next_states).max(1, keepdim=True)
V_next_state[non_final_mask] = self.target_Qnet(non_final_next_states).gather(1, next_state_actions)
else:
V_next_state[non_final_mask] = self.target_Qnet(non_final_next_states).max(1)[0]

You obtain next_state_actions from Qnet, but the following webpage suggests that we should get next_state_actions from target_Qnet. May I know if there is any theory support for your choice or it's simply an error you made? Very much appreciated.

Website: https://towardsdatascience.com/double-deep-q-networks-905dd8325412

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