This short course will introduce basic concepts of reinforcement learning in a nutshell. Slides will be made in English and lectures will be given by Bolei in Chinese. The course is for entertainment only.
The short course will be arranged as follows. Lectures 1-8 will be the foundation, the others will be the advanced topics, which are optional.
| Topic | Resources | |
|---|---|---|
| Lecture 1 | Overview | slide, video1, video2 |
| Lecture 2 | Markov Decision Process | |
| Lecture 3 | Model-free Prediction and Control | |
| Lecture 4 | On-policy and Off-policy Learning | |
| Lecture 5 | Value Function Approximation | |
| Lecture 6 | Policy Optimization: Basics | |
| Lecture 7 | Policy Optimization: State of the art | |
| Lecture 8 | Model-based RL | |
| Lecture 9 | Imitation Learning | |
| Lecture 10 | Distributed computing and RL system design | |
| Lecture 11 | Case Study on AlphaGo Series | |
| Lecture 12 | Case Study on AlphaStar and OpenAI Five | |
| Lecture 13 | Unsupervised Learning | |
| Lecture 14 | Generative Modeling | |
| TBD | ... |