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Lecture Notes for Reinforcement Learning

Chapter 1: An Introduction

Chapter 2: Multi-armed Bandits

Chapter 3: Markov Decision Processes

Chapter 4: Dynamic Programming

Chapter 5: Monte Carlo Methods