Reinforcement Learning: An Introduction

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Table of Contents

• Chapter 1: Introduction
• Chapter 2: Multi-armed Bandits
• Chapter 3: Finite Markov Decision Processes
• Chapter 4: Dynamic Programming
• Chapter 5: Monte Carlo Methods
• Chapter 6: Temporal-Difference Learning
• Chapter 7: n-step Bootstrapping
• Chapter 8: Planning and Learning with Tabular Methods
• Chapter 9: On-Policy Prediction with Approximation
• Chapter 10: On-Policy Control with Approximation
• Chapter 11: Off-Policy Methods with Approximation
• Chapter 12: Eligibility Traces
• Chapter 13: Policy Gradient Methods
• Chapter 14: Psychology
• Chapter 15: Neuroscience
• Chapter 16: Applications and Case Studies
• Chapter 17: Frontiers