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Hi, first off great work on making Stable-Baselines3 an excellent resource for deep reinforcement learning practitioners.
I noticed that your DQN implementation features a target q network which resembles Google's Deep Mind paper, Deep Reinforcement Learning with Double Q-learning. Meanwhile, Neural Fitted Q Iteration, by Riedmiller, calculates the target using the "current estimate" of the Q function. I am looking for clarification if DQN is truly a Double DQN. I hope to use this information to accurately hopefully implement prioritized experience replay based off of your DQN implementation.
Thanks,
Oliver
Checklist
I have checked that there is no similar issue in the repo
It is not, current DQN is the vanilla DQN (cf. doc), but we are working (and welcome help) on #622 (there is a PR for PER).
However, we do provide QR-DQN in SB3 contrib repo.
❓ Question
Hi, first off great work on making Stable-Baselines3 an excellent resource for deep reinforcement learning practitioners.
I noticed that your DQN implementation features a target q network which resembles Google's Deep Mind paper, Deep Reinforcement Learning with Double Q-learning. Meanwhile, Neural Fitted Q Iteration, by Riedmiller, calculates the target using the "current estimate" of the Q function. I am looking for clarification if DQN is truly a Double DQN. I hope to use this information to accurately hopefully implement prioritized experience replay based off of your DQN implementation.
Thanks,
Oliver
Checklist
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