update readme
This commit is contained in:
qiwang067
@@ -10,7 +10,7 @@
|
||||
|
||||
|
||||
## 笔记在线阅读地址(内容实时更新)
|
||||
地址：https://datawhalechina.github.io/leedeeprl-notes/
|
||||
地址：https://datawhalechina.github.io/easy-rl/
|
||||
|
||||
## 课程在线观看地址
|
||||
- bilibili：[李宏毅《深度强化学习》](https://www.bilibili.com/video/BV1MW411w79n)
|
||||
@@ -18,35 +18,36 @@
|
||||
## 内容导航
|
||||
| 章节 | 习题 | 项目 |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
|
||||
| [第一章 强化学习概述](https://datawhalechina.github.io/leedeeprl-notes/#/chapter1/chapter1) | [第一章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter1/chapter1_questions&keywords) | |
|
||||
| [第二章 马尔可夫决策过程 (MDP)](https://datawhalechina.github.io/leedeeprl-notes/#/chapter2/chapter2) | [第二章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter2/chapter2_questions&keywords) | |
|
||||
| [第三章 表格型方法](https://datawhalechina.github.io/leedeeprl-notes/#/chapter3/chapter3) | [第三章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter3/chapter3_questions&keywords) | [项目一 使用 Q-learning 解决悬崖寻路问题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter3/project1) |
|
||||
| [第四章 策略梯度](https://datawhalechina.github.io/leedeeprl-notes/#/chapter4/chapter4) | [第四章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter4/chapter4_questions&keywords) | |
|
||||
| [第五章 近端策略优化 (PPO) 算法](https://datawhalechina.github.io/leedeeprl-notes/#/chapter5/chapter5) | [第五章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter5/chapter5_questions&keywords) | |
|
||||
| [第六章 DQN (基本概念)](https://datawhalechina.github.io/leedeeprl-notes/#/chapter6/chapter6) | [第六章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter6/chapter6_questions&keywords) | |
|
||||
| [第七章 DQN (进阶技巧)](https://datawhalechina.github.io/leedeeprl-notes/#/chapter7/chapter7) | [第七章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter7/chapter7_questions&keywords) | [项目二 使用 DQN 实现 CartPole-v0](https://datawhalechina.github.io/leedeeprl-notes/#/chapter7/project2) |
|
||||
| [第八章 DQN (连续动作)](https://datawhalechina.github.io/leedeeprl-notes/#/chapter8/chapter8) | [第八章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter8/chapter8_questions&keywords) | |
|
||||
| [第九章 演员-评论家算法](https://datawhalechina.github.io/leedeeprl-notes/#/chapter9/chapter9) | [第九章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter9/chapter9_questions&keywords) | |
|
||||
| [第十章 稀疏奖励](https://datawhalechina.github.io/leedeeprl-notes/#/chapter10/chapter10) | [第十章 习题](https://datawhalechina.github.io/leedeeprl-notes/#/chapter10/chapter10_questions&keywords) | |
|
||||
| [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) |||
|
||||
| [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) | |
|
||||
| [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) | |
|
||||
| [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) |
|
||||
| [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) | |
|
||||
| [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) | |
|
||||
| [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) | |
|
||||
| [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) |
|
||||
| [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) | |
|
||||
| [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) | |
|
||||
| [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) | |
|
||||
| [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) | |
|
||||
| [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) |
|
||||
| [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/leedeeprl-notes/tree/master/codes/MonteCarlo) | | 蒙特卡洛算法 | OK |
|
||||
| [Q-Learning](https://github.com/datawhalechina/leedeeprl-notes/tree/master/codes/QLearning) | | | OK |
|
||||
| [Sarsa](https://github.com/datawhalechina/leedeeprl-notes/tree/master/codes/Sarsa) | | | OK |
|
||||
| [DQN](https://github.com/datawhalechina/leedeeprl-notes/tree/master/codes/DQN) | [DQN-paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | | OK |
|
||||
| [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/leedeeprl-notes/tree/master/codes/DoubleDQN) | | | 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/leedeeprl-notes/tree/master/codes/PolicyGradient) | | | OK |
|
||||
| [A2C](https://github.com/datawhalechina/leedeeprl-notes/tree/master/codes/A2C) | | | OK |
|
||||
| [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) | | |
|
||||
|
||||
|
||||
## 贡献者
|
||||
|
||||
<table border="0">
|
||||
|
||||
Reference in New Issue
Block a user