Reinforcement Learning: An Introduction
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 learning.
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Review: Reinforcement Learning: An IntroductionUser Review - Jon Gauthier - Goodreads
Despite its age, this book is still the canonical introduction to reinforcement learning. I'm reading parts as necessary — not sure if I'll ever read cover-to-cover. In any case this has been an ... Read full review
Review: Reinforcement Learning: An IntroductionUser Review - Rami alaa - Goodreads
And I read it again actually I'm reading the HTML version Read full review
Elementary Solution Methods
A Unified View
Generalization and Function Approximation
Planning and Learning
Dimensions of Reinforcement Learning
Summary of Notation