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| [第十一章 模仿学习](https://datawhalechina.github.io/leedeeprl-notes/#/chapter11/chapter11) | [第十一章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter11/chapter11_questions&keywords) | |
| [第十二章 深度确定性策略梯度 (DDPG) 算法](https://datawhalechina.github.io/leedeeprl-notes/#/chapter12/chapter12) | [第十二章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter12/chapter12_questions&keywords) | [项目三 使用 Policy-Based 方法实现 Pendulum-v0](https://datawhalechina.github.io/leedeeprl-notes/#/chapter12/project3) |
| [第十三章 AlphaStar 论文解读](https://datawhalechina.github.io/leedeeprl-notes/#/chapter13/chapter13) |||
## 算法代码实现一览
| 算法名称 | 相关论文材料 | 备注 | 进度 |
| :----------------------: | :---------------------------------------------------------: | :--------------------------------: | :--: |
| On-Policy First-Visit MC | | 蒙特卡洛算法 | OK |
| Q-Learning | | | OK |
| SARSA | | | OK |
| DQN | [DQN-paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | | OK |
| 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 |
| DoubleDQN | | | OK |
| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
| PolicyGradient | | | OK |
| A2C | | | OK |
| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | | OK |
| TD3 | [Twin Dueling DDPG Paper](https://arxiv.org/abs/1802.09477) | | |
## 贡献者
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