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| [第十三章 AlphaStar 论文解读](https://datawhalechina.github.io/easy-rl/#/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](https://github.com/datawhalechina/easy-rl/tree/master/codes/MonteCarlo) | | 蒙特卡洛算法 | OK |
| [Q-Learning](https://github.com/datawhalechina/easy-rl/tree/master/codes/QLearning) | | | OK |
| [Sarsa](https://github.com/datawhalechina/easy-rl/tree/master/codes/Sarsa) | | | OK |
| [DQN](https://github.com/datawhalechina/easy-rl/tree/master/codes/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](https://github.com/datawhalechina/easy-rl/tree/master/codes/DoubleDQN) | | | OK |
| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
| [PolicyGradient](https://github.com/datawhalechina/easy-rl/tree/master/codes/PolicyGradient) | | | OK |
| [A2C](https://github.com/datawhalechina/easy-rl/tree/master/codes/A2C) | | | OK |
| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | | OK |
| TD3 | [Twin Dueling DDPG Paper](https://arxiv.org/abs/1802.09477) | | |
| 算法名称 | 相关论文材料 | 备注 | 进度 |
| :----------------------------------------------------------: | :---------------------------------------------------------: | :----------------------------------------------------------: | :--: |
| [On-Policy First-Visit MC](https://github.com/datawhalechina/easy-rl/tree/master/codes/MonteCarlo) | | 蒙特卡洛算法 | OK |
| [Q-Learning](https://github.com/datawhalechina/easy-rl/tree/master/codes/QLearning) | | | OK |
| [Sarsa](https://github.com/datawhalechina/easy-rl/tree/master/codes/Sarsa) | | | OK |
| [DQN](https://github.com/datawhalechina/easy-rl/tree/master/codes/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](https://github.com/datawhalechina/easy-rl/tree/master/codes/DoubleDQN) | | | OK |
| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
| [PolicyGradient](https://github.com/datawhalechina/easy-rl/tree/master/codes/PolicyGradient) | | | OK |
| [A2C](https://github.com/datawhalechina/easy-rl/tree/master/codes/A2C) | | | OK |
| [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 |
| 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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docs/README.md
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@@ -30,20 +30,20 @@
| [第十三章 AlphaStar 论文解读](https://datawhalechina.github.io/easy-rl/#/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](https://github.com/datawhalechina/easy-rl/tree/master/codes/MonteCarlo) | | 蒙特卡洛算法 | OK |
| [Q-Learning](https://github.com/datawhalechina/easy-rl/tree/master/codes/QLearning) | | | OK |
| [Sarsa](https://github.com/datawhalechina/easy-rl/tree/master/codes/Sarsa) | | | OK |
| [DQN](https://github.com/datawhalechina/easy-rl/tree/master/codes/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](https://github.com/datawhalechina/easy-rl/tree/master/codes/DoubleDQN) | | | OK |
| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
| [PolicyGradient](https://github.com/datawhalechina/easy-rl/tree/master/codes/PolicyGradient) | | | OK |
| [A2C](https://github.com/datawhalechina/easy-rl/tree/master/codes/A2C) | | | OK |
| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | | OK |
| TD3 | [Twin Dueling DDPG Paper](https://arxiv.org/abs/1802.09477) | | |
| 算法名称 | 相关论文材料 | 备注 | 进度 |
| :----------------------------------------------------------: | :---------------------------------------------------------: | :----------------------------------------------------------: | :--: |
| [On-Policy First-Visit MC](https://github.com/datawhalechina/easy-rl/tree/master/codes/MonteCarlo) | | 蒙特卡洛算法 | OK |
| [Q-Learning](https://github.com/datawhalechina/easy-rl/tree/master/codes/QLearning) | | | OK |
| [Sarsa](https://github.com/datawhalechina/easy-rl/tree/master/codes/Sarsa) | | | OK |
| [DQN](https://github.com/datawhalechina/easy-rl/tree/master/codes/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](https://github.com/datawhalechina/easy-rl/tree/master/codes/DoubleDQN) | | | OK |
| Hierarchical DQN | [Hierarchical DQN](https://arxiv.org/abs/1604.06057) | | |
| [PolicyGradient](https://github.com/datawhalechina/easy-rl/tree/master/codes/PolicyGradient) | | | OK |
| [A2C](https://github.com/datawhalechina/easy-rl/tree/master/codes/A2C) | | | OK |
| [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 |
| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | | OK |
| TD3 | [Twin Dueling DDPG Paper](https://arxiv.org/abs/1802.09477) | | |
## 贡献者