Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

Sutton, Richard S.; Barto, Andrew G.

ISBN 10: 0262193981 / ISBN 13: 9780262193986

Editore: Bradford Books, 1998

Riguardo questo articolo

Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Codice articolo M00262193981-V

Dati bibliografici

• Titolo: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
• Editore: Bradford Books
• Data di pubblicazione: 1998
• Lingua: Inglese
• ISBN 10: 0262193981
• ISBN 13: 9780262193986
• Rilegatura: Rilegato
• Condizione: very good

Descrizione articolo

Riassunto

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.

Informazioni sugli autori

Andrew G. Barto is Professor of Computer Science at the University of Massachusetts.

Richard S. Sutton is Senior Research Scientist, Department of Computer Science, University of Massachusetts.