From d4690c205837fd9ec23199e41cd133dd477903e1 Mon Sep 17 00:00:00 2001 From: JohnJim0816 Date: Tue, 23 Mar 2021 16:07:30 +0800 Subject: [PATCH] update README --- README.md | 27 ++++++++++++++------------- docs/README.md | 28 ++++++++++++++-------------- 2 files changed, 28 insertions(+), 27 deletions(-) diff --git a/README.md b/README.md index 485f297..99328a2 100644 --- a/README.md +++ b/README.md @@ -30,19 +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 diff --git a/docs/README.md b/docs/README.md index ec45de7..99328a2 100644 --- a/docs/README.md +++ b/docs/README.md @@ -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