【代码集合】深度强化学习Pytorch实现集锦

本次分享的是用PyTorch语言编写的深度强化学习算法的高质量实现，这些IPython笔记本的目的主要是帮助练习和理解这些论文；因此，在某些情况下，我将选择可读性而不是效率。首先，我会上传论文的实现，然后是标记来解释代码的每一部分。

相关论文

1. Human Level Control Through Deep Reinforement Learning

    [Publication] https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning/

    [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/01.DQN.ipynb

2. Multi-Step Learning (from Reinforcement Learning: An Introduction, Chapter 7) 

   [Publication] https://github.com/qfettes/DeepRL-Tutorials/blob/master/01.DQN.ipynb

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/02.NStep_DQN.ipynb

3. Deep Reinforcement Learning with Double Q-learning 

   [Publication] https://arxiv.org/abs/1509.06461

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/03.Double_DQN.ipynb

4. Dueling Network Architectures for Deep Reinforcement Learning 

   [Publication] https://arxiv.org/abs/1511.06581

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/04.Dueling_DQN.ipynb

5. Noisy Networks for Exploration 

   [Publication] https://github.com/qfettes/DeepRL-Tutorials/blob/master/04.Dueling_DQN.ipynb

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/05.DQN-NoisyNets.ipynb

6. Prioritized Experience Replay 

   [Publication] https://arxiv.org/abs/1511.05952?context=cs

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/06.DQN_PriorityReplay.ipynb

7. A Distributional Perspective on Reinforcement Learning 

   [Publication] https://arxiv.org/abs/1707.06887

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/07.Categorical-DQN.ipynb

8. Rainbow: Combining Improvements in Deep Reinforcement Learning 

   [Publication] https://arxiv.org/abs/1710.02298

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/08.Rainbow.ipynb

9. Distributional Reinforcement Learning with Quantile Regression 

   [Publication] https://arxiv.org/abs/1710.10044

   [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/09.QuantileRegression-DQN.ipynb

10. Rainbow with Quantile Regression 

    [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/10.Quantile-Rainbow.ipynb

11. Deep Recurrent Q-Learning for Partially Observable MDPs 

    [Publication] https://arxiv.org/abs/1507.06527

    [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/11.DRQN.ipynb

12. Advantage Actor Critic (A2C) 

    [Publication1] https://arxiv.org/abs/1602.01783

    [Publication2] https://blog.openai.com/baselines-acktr-a2c/

    [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/12.A2C.ipynb

13. High-Dimensional Continuous Control Using Generalized Advantage Estimation 

    [Publication] https://arxiv.org/abs/1506.02438

    [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/13.GAE.ipynb

14. Proximal Policy Optimization Algorithms 

    [Publication] https://arxiv.org/abs/1707.06347

    [code] https://github.com/qfettes/DeepRL-Tutorials/blob/master/14.PPO.ipynb

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