**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.** 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. Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers. 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 problems. Part II provides basic solution methods: dynamic programming, Monte Carlo simulation, 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. 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.--book Jacket. Contents -- Series Foreword -- Preface -- I. The Problem -- 1. Introduction -- 2. Evaluative Feedback -- 3. The Reinforcement Learning Problem -- Ii. Elementary Solution Methods -- 4. Dynamic Programming -- 5. Monte Carlo Methods -- 6. Temporal-difference Learning -- Iii. A Unified View -- 7. Eligibility Traces -- 8. Generalization And Function Approximation -- 9. Planning And Learning -- 10. Dimensions Of Reinforcement Learning -- 11. Case Studies -- References -- Summary Of Notation -- Index. Richard S. Sutton And Andrew G. Barto. Includes Bibliographical References (p. [291]-312) And Index. Reinforcement learning is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with its environment. This book explains the main ideas and algorithms of reinforcement learning. The book is thorough in its coverage. 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. Presents the book "Reinforcement Learning: An Introduction," written by Richard S. Sutton and Andrew G. Barto and published by the Massachusetts Institute of Technology (MIT) Press in 1998. The book is a textbook targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems. Examines a computation approach to learning from interaction with environment
