This repository contains DQN related algorithms implemented in PyTorch. There are tutorial videos in Chinese on bilibili.

• Algorithm: DQN
• Original paper: Human-level control through deep reinforcement learning
• Reference code: https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
• Tutorial video: https://www.bilibili.com/video/BV1Rq4y1b7ML/

It contains 2 codes that dqn_Nature_gpu.py for training and dqn_Nature_eval.py for testing the model. You can run the code directly to train or test, but please be noticed of the path for saving or loading .pth files.

• Algorithm: Double Q-Learning
• Original paper: Deep Reinforcement Learning with Double Q-learning
• Reference code: https://github.com/deepmind/dqn_zoo
• Tutorial video: https://www.bilibili.com/video/BV1i94y1X7bt/

It refers to the code named dqn_zoo implemented by DeepMind in Jax. Double_dqn in this repository is converted from Jax to PyTorch. You can run main.py with the config variable --mode to choose train or eval. It is able to restart training at breakpoints by modifying the variable net_file in main.py.

• The working directory needs to be configured of DQN. It is recommended to run code on PyCharm that the configuration for running codes would be easier, referring to the picture below.

• If net_file(.pth) can not be found, the training process will start at the begining rather than breakpoints.

• Algorithm: Prioritized Experience Replay
• Original paper: Prioritized Experience Replay
• Reference code: https://github.com/deepmind/dqn_zoo
• Tutorial video: https://www.bilibili.com/video/BV1nG4y1S7db/

Its code structure is similar to double_dqn. You can take steps above in double_dqn to run the code.

gym==0.19.0
numpy==1.21.6
dm-env==1.5
torch==1.13.1
pyglet==1.5.21