Reinforcement Learning: An IntroductionReinforcement learning, one of the most active research areas in artificialintelligence, is a computational approach to learning whereby an agent tries to maximize the totalamount of reward it receives when interacting with a complex, uncertain environment. InReinforcement Learning, Richard Sutton and Andrew Barto provide a clear andsimple account of the key ideas and algorithms of reinforcement learning. Their discussion rangesfrom the history of the field's intellectual foundations to the most recent developments andapplications. The only necessary mathematical background is familiarity with elementary concepts ofprobability. The book is divided into three parts. Part I defines thereinforcement learning problem in terms of Markov decision processes. Part II provides basicsolution methods: dynamic programming, Monte Carlo methods, and temporaldifference learning. PartIII 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 thefuture of reinforcement learning. 
What people are saying  Write a review
User ratings
5 stars 
 
4 stars 
 
3 stars 
 
2 stars 
 
1 star 

Review: Reinforcement Learning: An Introduction
User Review  Rami alaa  GoodreadsAnd I read it again actually I'm reading the HTML version Read full review
Review: Reinforcement Learning: An Introduction
User Review  Fabio Zambetta  GoodreadsThat's where you want to start reading about RL. Read full review
Contents
Introduction  3 
Evaluative Feedback  25 
Elementary Solution Methods  87 
A Unified View  161 
Generalization and Function Approximation  193 
Planning and Learning  227 
Dimensions of Reinforcement Learning  255 
Case Studies  261 
References  291 
Summary of Notation  313 