Introduction-of-Reinforcement-Learning

강화학습 입문자를 위한 필요 개념과 내용들을 아래의 references 내용을 바탕으로 정리하였음.

References

David silver의 reinforcement learning (http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
Pang-Yo Lab의 강화학습의 기초 이론 (https://www.youtube.com/watch?v=wYgyiCEkwC8&list=PLpRS2w0xWHTcTZyyX8LMmtbcMXpd3s4TU)
Richard S. Sutton and Andrew G. Barto의 Reinforcement Learning, An introduction second edition

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
Chap 1. Introduction to Reinforcement Learning.pdf		Chap 1. Introduction to Reinforcement Learning.pdf
Chap 2. Markov Decision Process.pdf		Chap 2. Markov Decision Process.pdf
Chap 3. Planning by Dynamic Programming.pdf		Chap 3. Planning by Dynamic Programming.pdf
Chap 4. Model-Free Prediction.pdf		Chap 4. Model-Free Prediction.pdf
Chap 5. Model-Free Control.pdf		Chap 5. Model-Free Control.pdf
Chap 6. Value Function Approximation.pdf		Chap 6. Value Function Approximation.pdf
Chap 7. Policy Gradient.pdf		Chap 7. Policy Gradient.pdf
Chap 8. Integrating Learning and Planning.pdf		Chap 8. Integrating Learning and Planning.pdf
Chap 9. Exploration and Exploitation.pdf		Chap 9. Exploration and Exploitation.pdf
Extra Chapter. Classic Games.pdf		Extra Chapter. Classic Games.pdf
README.md		README.md