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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 4675 |
| 2026-08-01 | 4675 |
| 2026-08-02 | 4675 |
| 2026-08-04 | 4676 |
| 2026-08-05 | 4675 |
| 2026-08-06 | 4676 |
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# Deep Reinforcement Learning
1. **Overview.**
* Reinforcement Learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_1.pdf)]
[[lecture note](https://github.com/wangshusen/DeepLearning/blob/master/LectureNotes/DRL/DRL.pdf)]
[[Video (in Chinese)](https://youtu.be/vmkRMvhCW5c)].
* Value-Based Learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_2.pdf)]
[[Video (in Chinese)](https://youtu.be/jflq6vNcZyA)].
* Policy-Based Learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_3.pdf)]
[[Video (in Chinese)](https://youtu.be/qI0vyfR2_Rc)].
* Actor-Critic Methods
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_4.pdf)]
[[Video (in Chinese)](https://youtu.be/xjd7Jq9wPQY)].
* AlphaGo
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_5.pdf)]
[[Video (in Chinese)](https://youtu.be/zHojAp5vkRE)].
2. **TD Learning.**
* Sarsa
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/2_TD_1.pdf)]
[[Video (in Chinese)](https://youtu.be/-cYWdUubB6Q)].
* Q-learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/2_TD_2.pdf)]
[[Video (in Chinese)](https://youtu.be/Ymy2w3DGn2U)].
* Multi-Step TD Target
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/2_TD_3.pdf)]
[[Video (in Chinese)](https://youtu.be/UqTP138IATc)].
3. **Advanced Topics on Value-Based Learning.**
* Experience Replay (ER) & Prioritized ER
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/3_DQN_1.pdf)]
[[Video (in Chinese)](https://youtu.be/rhslMPmj7SY)].
* Overestimation, Target Network, & Double DQN
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/3_DQN_2.pdf)]
[[Video (in Chinese)](https://youtu.be/X2-56QN79zc)].
* Dueling Networks
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/3_DQN_3.pdf)]
[[Video (in Chinese)](https://youtu.be/DBux6cA0EoM)].
4. **Policy Gradient with Baseline.**
* Policy Gradient with Baseline
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_1.pdf)]
[[Video (in Chinese)](https://youtu.be/yNEqbptitZs)].
* REINFORCE with Baseline
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_2.pdf)]
[[Video (in Chinese)](https://youtu.be/Ob78ADXTQNo)].
* Advantage Actor-Critic (A2C)
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_3.pdf)]
[[Video (in Chinese)](https://youtu.be/mtT4TSGSon8)].
* REINFORCE versus A2C
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_4.pdf)]
[[Video (in Chinese)](https://youtu.be/hN9WMIMMeAI)].
5. **Advanced Topics on Policy-Based Learning.**
* Trust-Region Policy Optimization (TRPO)
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/5_Policy_1.pdf)]
[[Video (in Chinese)](https://youtu.be/fcSYiyvPjm4)].
* Partial Observation and RNNs.
6. **Dealing with Continuous Action Space.**
* Discrete versus Continuous Control
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/6_Continuous_1.pdf)]
[[Video (in Chinese)](https://youtu.be/rRIjgdxSvg8)].
* Deterministic Policy Gradient (DPG) for Continuous Control
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/6_Continuous_2.pdf)]
[[Video (in Chinese)](https://youtu.be/cmWejKRWLA8)].
* Stochastic Policy Gradient for Continuous Control
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/6_Continuous_3.pdf)]
[[Video (in Chinese)](https://youtu.be/McqFyl_W5Wc)].
7. **Multi-Agent Reinforcement Learning.**
* Basics and Challenges
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/7_MARL_1Excerpt of 4,377 characters
Read on GitHub
Shusen Wang · United States
58
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a7978fdf3db6183e, desc:reinforcement learning