RL-Coursera

서튼 교수 강화학습 교재 공부

• Sutton & Barto, Reinforcement learning: an introduction, 2e
• 질문(?)과 코멘트(!) 작성
• 시뮬레이션 코드 리팩토링 (CleanRL 처럼)

Contents

1. introduction

part I. tabular solution methods

2. multi-armed bandits
3. finite Markov decision processes
4. dynamic programming
5. Monte Carlo methods
6. temporal-difference learning
7. n-step bootstrapping
8. planning and learning with tabular methods

part II. approximate solution methods

9. on-policy prediction with approximation
10. on-policy control with approximation
11. off-policy methods with approximation
12. eligibility traces
13. policy gradient methods

part III. looking deeper

14. psychology
15. neuroscience
16. applications and case studies 17.frontiers