| Topic |
Videos (on Canvas/Panopto) |
Course Materials |
|
| Introduction to Reinforcement Learning |
|
- Lecture 1 Slides Post class version
- Additional Materials:
|
| Tabular MDP planning |
|
- Lecture 2 Slides [Post class, annotated]
- Additional Materials:
|
| Tabular RL policy evaluation |
|
- Lecture 3 Slides (pre-class) [Post class, with annotations]
- Additional Materials:
|
| Q-learning |
|
- Lecture 4 Slides (post class with annotations)
- Additional Materials:
|
| RL with function approximation |
|
- Lecture 5 Slides [Post lecture with annotations]
- Lecture 6 Slides [Post class annotations]
- Lecture 7 Slides [Post class annotations]
- Additional Materials:
|
| Policy search |
|
- Lecture 8 Slides
- Lecture 9 Slides [Post class]
- Additional Materials:
|
| Fast Learning |
|
- Lecture 10 Slides [Post class with annotations]
- Lecture 11 Slides [Post class, with annotations]
- Lecture 12 Slides [Post class, with annotations]
- Additional Materials:
|
| Batch Reinforcement Learning |
|
- Imitation Learning Slides [Post class, with annotations]
- Batch Policy Learning [Post class, with annotations]
- Reinforcement Learning and Reward
- Additional Materials:
|
