# 经典强化学习论文解读

该部分是蘑菇书的扩展内容，**整理&总结&解读强化学习领域的经典论文**。主要有DQN类、策略梯度类、模仿学习类、分布式强化学习、多任务强化学习、探索策略、分层强化学习以及其他技巧等方向的论文。后续会配有视频解读（与WhalePaper合作），会陆续上线Datawhale B站公众号。

每周更新5篇左右的论文，欢迎关注。

**转发请加上链接&来源（Easy-RL项目）**

| 类别            | 论文题目                                                     | 原文链接                                      | 视频解读 |
| --------------- | ------------------------------------------------------------ | --------------------------------------------- | -------------------- |
| DQN             | Playing Atari with Deep Reinforcement Learning (**DQN**)               | https://arxiv.org/abs/1312.5602               |                      |
|                 | Deep Recurrent Q-Learning for Partially Observable MDPs      | https://arxiv.org/abs/1507.06527              |                      |
|                 | Dueling Network Architectures for Deep Reinforcement Learning (**Dueling DQN**) | https://arxiv.org/abs/1511.06581              |                      |
|                 | Deep Reinforcement Learning with Double Q-learning (**Double DQN**) | https://arxiv.org/abs/1509.06461              |                      |
|                 | Prioritized Experience Replay (**PER**)                      | https://arxiv.org/abs/1511.05952              |                      |
|                 | Rainbow: Combining Improvements in Deep Reinforcement Learning (**Rainbow**) | https://arxiv.org/abs/1710.02298              |                      |
| Policy gradient | Asynchronous Methods for Deep Reinforcement Learning (**A3C**) | https://arxiv.org/abs/1602.01783              |                      |
|                 | Trust Region Policy Optimization (**TRPO**)                  | https://arxiv.org/abs/1502.05477              |                      |
|                 | High-Dimensional Continuous Control Using Generalized Advantage Estimation (**GAE**) | https://arxiv.org/abs/1506.02438              |                      |
|                 | Proximal Policy Optimization Algorithms (**PPO**)            | https://arxiv.org/abs/1707.06347              |                      |
|                 | Emergence of Locomotion Behaviours in Rich Environments (**PPO-Penalty**) | https://arxiv.org/abs/1707.02286              |                      |
|                 | Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (**ACKTP**) | https://arxiv.org/abs/1708.05144              |                      |
|                 | Sample Efficient Actor-Critic with Experience Replay (**ACER**) | https://arxiv.org/abs/1611.01224              |                      |
|                 | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor(**SAC**) | https://arxiv.org/abs/1801.01290              |                      |
|                 | Deterministic Policy Gradient Algorithms (**DPG**)           | http://proceedings.mlr.press/v32/silver14.pdf |                      |
|                 | Continuous Control With Deep Reinforcement Learning (**DDPG**) | https://arxiv.org/abs/1509.02971              |                      |
|                 | Addressing Function Approximation Error in Actor-Critic Methods (**TD3**) | https://arxiv.org/abs/1802.09477              |                      |
|                 | A Distributional Perspective on Reinforcement Learning (**C51**) | https://arxiv.org/abs/1707.06887              |                      |
|                 |                                                              |                                               |                      |