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JohnJim0816
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地址：https://datawhalechina.github.io/easy-rl/
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## 内容导航
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| 章节 | 习题 | 项目 |
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| 章节 | 习题 | 相关项目 |
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| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| [第一章 强化学习概述](https://datawhalechina.github.io/easy-rl/#/chapter1/chapter1) | [第一章 习题](https://datawhalechina.github.io/easy-rl/#/chapter1/chapter1_questions&keywords) | |
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| [第二章 马尔可夫决策过程 (MDP)](https://datawhalechina.github.io/easy-rl/#/chapter2/chapter2) | [第二章 习题](https://datawhalechina.github.io/easy-rl/#/chapter2/chapter2_questions&keywords) | |
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| [第三章 表格型方法](https://datawhalechina.github.io/easy-rl/#/chapter3/chapter3) | [第三章 习题](https://datawhalechina.github.io/easy-rl/#/chapter3/chapter3_questions&keywords) | [项目一 使用 Q-learning 解决悬崖寻路问题](https://datawhalechina.github.io/easy-rl/#/chapter3/project1) |
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| [第三章 表格型方法](https://datawhalechina.github.io/easy-rl/#/chapter3/chapter3) | [第三章 习题](https://datawhalechina.github.io/easy-rl/#/chapter3/chapter3_questions&keywords) | [Q-learning算法实战](https://datawhalechina.github.io/easy-rl/#/chapter3/project1) |
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| [第四章 策略梯度](https://datawhalechina.github.io/easy-rl/#/chapter4/chapter4) | [第四章 习题](https://datawhalechina.github.io/easy-rl/#/chapter4/chapter4_questions&keywords) | |
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| [第五章 近端策略优化 (PPO) 算法](https://datawhalechina.github.io/easy-rl/#/chapter5/chapter5) | [第五章 习题](https://datawhalechina.github.io/easy-rl/#/chapter5/chapter5_questions&keywords) | |
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| [第六章 DQN (基本概念)](https://datawhalechina.github.io/easy-rl/#/chapter6/chapter6) | [第六章 习题](https://datawhalechina.github.io/easy-rl/#/chapter6/chapter6_questions&keywords) | |
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| [第七章 DQN (进阶技巧)](https://datawhalechina.github.io/easy-rl/#/chapter7/chapter7) | [第七章 习题](https://datawhalechina.github.io/easy-rl/#/chapter7/chapter7_questions&keywords) | [项目二 使用 DQN 实现 CartPole-v0](https://datawhalechina.github.io/easy-rl/#/chapter7/project2) |
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| [第七章 DQN (进阶技巧)](https://datawhalechina.github.io/easy-rl/#/chapter7/chapter7) | [第七章 习题](https://datawhalechina.github.io/easy-rl/#/chapter7/chapter7_questions&keywords) | [DQN算法实战](https://datawhalechina.github.io/easy-rl/#/chapter7/project2) |
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| [第八章 DQN (连续动作)](https://datawhalechina.github.io/easy-rl/#/chapter8/chapter8) | [第八章 习题](https://datawhalechina.github.io/easy-rl/#/chapter8/chapter8_questions&keywords) | |
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| [第九章 演员-评论家算法](https://datawhalechina.github.io/easy-rl/#/chapter9/chapter9) | [第九章 习题](https://datawhalechina.github.io/easy-rl/#/chapter9/chapter9_questions&keywords) | |
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| [第十章 稀疏奖励](https://datawhalechina.github.io/easy-rl/#/chapter10/chapter10) | [第十章 习题](https://datawhalechina.github.io/easy-rl/#/chapter10/chapter10_questions&keywords) | |
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| [第十一章 模仿学习](https://datawhalechina.github.io/easy-rl/#/chapter11/chapter11) | [第十一章 习题](https://datawhalechina.github.io/easy-rl/#/chapter11/chapter11_questions&keywords) | |
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| [第十二章 深度确定性策略梯度 (DDPG) 算法](https://datawhalechina.github.io/easy-rl/#/chapter12/chapter12) | [第十二章 习题](https://datawhalechina.github.io/easy-rl/#/chapter12/chapter12_questions&keywords) | [项目三 使用 Policy-Based 方法实现 Pendulum-v0](https://datawhalechina.github.io/easy-rl/#/chapter12/project3) |
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| [第十三章 AlphaStar 论文解读](https://datawhalechina.github.io/easy-rl/#/chapter13/chapter13) |||
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## 算法代码实现一览
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| [第十二章 深度确定性策略梯度 (DDPG) 算法](https://datawhalechina.github.io/easy-rl/#/chapter12/chapter12) | [第十二章 习题](https://datawhalechina.github.io/easy-rl/#/chapter12/chapter12_questions&keywords) | [DDPG算法实战](https://datawhalechina.github.io/easy-rl/#/chapter12/project3) |
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| [第十三章 AlphaStar 论文解读](https://datawhalechina.github.io/easy-rl/#/chapter13/chapter13) | | |
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## 算法实战
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| 算法名称 | 相关论文材料 | 备注 | 进度 |
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| :----------------------------------------------------------: | :---------------------------------------------------------: | :----------------------------------------------------------: | :--: |
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| [On-Policy First-Visit MC](https://github.com/datawhalechina/easy-rl/tree/master/codes/MonteCarlo) | | 蒙特卡洛算法 | OK |
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| [Q-Learning](https://github.com/datawhalechina/easy-rl/tree/master/codes/QLearning) | | | OK |
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| [Sarsa](https://github.com/datawhalechina/easy-rl/tree/master/codes/Sarsa) | | | OK |
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| [DQN](https://github.com/datawhalechina/easy-rl/tree/master/codes/DQN) | [DQN-paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | | OK |
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| DQN-cnn | [DQN-paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | 与DQN相比使用了CNN而不是全链接网络 | OK |
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| [DoubleDQN](https://github.com/datawhalechina/easy-rl/tree/master/codes/DoubleDQN) | | | OK |
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| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
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| [PolicyGradient](https://github.com/datawhalechina/easy-rl/tree/master/codes/PolicyGradient) | | | OK |
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| [A2C](https://github.com/datawhalechina/easy-rl/tree/master/codes/A2C) | | | OK |
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| [PPO](https://github.com/datawhalechina/easy-rl/tree/master/codes/PPO) | [PPO paper](https://arxiv.org/abs/1707.06347) | [PPO算法实战](https://blog.csdn.net/JohnJim0/article/details/115126363) | OK |
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| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | | OK |
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| TD3 | [Twin Dueling DDPG Paper](https://arxiv.org/abs/1802.09477) | | |
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| 算法名称 | 相关论文材料 | 环境 | 备注 |
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| :--------------------------------------: | :---------------------------------------------------------: | ------------------------------------- | :--------------------------------: |
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| [On-Policy First-Visit MC](../codes/MonteCarlo) | | [Racetrack](../codes/envs/racetrack_env.md) | |
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| [Q-Learning](../codes/QLearning) | | [CliffWalking-v0](../codes/envs/gym_info.md) | |
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| [Sarsa](../codes/Sarsa) | | [Racetrack](../codes/envs/racetrack_env.md) | |
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| [DQN](../codes/DQN) | [DQN-paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [CartPole-v0](../codes/envs/gym_info.md) | |
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| DQN-cnn | [DQN-paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [CartPole-v0](../codes/envs/gym_info.md) | 与DQN相比使用了CNN而不是全链接网络 |
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| [DoubleDQN](../codes/DoubleDQN) | | [CartPole-v0](../codes/envs/gym_info.md) | 效果不好，待改进 |
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| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
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| [PolicyGradient](../codes/PolicyGradient) | | [CartPole-v0](../codes/envs/gym_info.md) | |
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| A2C | | [CartPole-v0](../codes/envs/gym_info.md) | |
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| A3C | | | |
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| SAC | | | |
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| [PPO](../codes/PPO) | [PPO paper](https://arxiv.org/abs/1707.06347) | [CartPole-v0](../codes/envs/gym_info.md) | |
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| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | [Pendulum-v0](../codes/envs/gym_info.md) | |
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| TD3 | [Twin Dueling DDPG Paper](https://arxiv.org/abs/1802.09477) | | |
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| GAIL | | | |
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## 贡献者
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<td>
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<a href="https://github.com/JohnJim0816"><img width="70" height="70" src="https://github.com/JohnJim0816.png?s=40" alt="pic"></a><br>
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<a href="https://github.com/JohnJim0816">John Jim</a>
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<p>项目设计<br> 北京大学</p>
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<p>算法实战<br> 北京大学</p>
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</td>
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</tr>
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</tbody>
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