Reinforcement Learning by Richard S Sutton

Reinforcement Learning by Richard S Sutton

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.

Barto, Andrew G.: - Andrew G. Barto is Professor Emeritus in the College of Computer and Information Sciences at the University of Massachusetts Amherst.

SKU	Unavailable
ISBN 13	9780262193986
ISBN 10	0262193981
Title	Reinforcement Learning
Author	Richard S Sutton
Series	Adaptive Computation And Machine Learning Series
Condition	Unavailable
Binding Type	Hardback
Publisher	MIT Press Ltd
Year published	1998-02-26
Number of pages	344
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.