Sutton R.S., Barto A.G. Reinforcement Learning: An Introduction

Second edition. — MIT Press, 2017. — 445 p.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning7Tabular Solution Methods
Multi-armed Bandits
Finite Markov Decision Processes
Dynamic Programming
Monte Carlo Methods
Temporal-Difference Learning
n-step Bootstrapping
Planning and Learning with Tabular Methods
Approximate Solution Methods
On-policy Prediction with Approximation
On-policy Control with Approximation
Off-policy Methods with Approximation
Eligibility Traces
Policy Gradient Methods
Looking Deeper
Psychology
Neuroscience
Applications and Case Studies
Frontiers