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

Sutton, Richard S.; Barto, Andrew G.

ISBN 10: 0262193981 / ISBN 13: 9780262193986

Edité par Bradford Books, 1998

A propos de cet article

Condition: 15 pages with some highlighted text, the rest excellent.

The book provides a clear and simple account of the key ideas and algorithms in this area of artificial intelligence, where an agent learns to maximize a cumulative reward by interacting with a complex, uncertain environment.

It covers the history of the field's intellectual foundations and proceeds to the core algorithms and concepts, including:

The Reinforcement Learning Problem framed in terms of Markov Decision Processes (MDPs).

Basic Solution Methods like Dynamic Programming, Monte Carlo methods, and the influential Temporal-Difference (TD) learning (e.g., Q-learning and SARSA).

Function Approximation for handling large state spaces, including the use of artificial neural networks.

More advanced topics like policy-gradient methods and a discussion of RL's relationships to psychology and neuroscience.

Often referred to as the "bible" of the field, it is a foundational text suitable for students, researchers, and practitioners with a basic understanding of probability.

N° de réf. du vendeur ABE-1760107744142

Détails bibliographiques

• Titre: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
• Éditeur: Bradford Books
• Date de parution: 1998
• Langue: anglais
• ISBN à 10 chiffres: 0262193981
• ISBN à 13 chiffres: 9780262193986
• Reliure: Couverture rigide
• Édition: Edition originale
• Impression: 1er tirage
• État de l'article: Assez bon
• Dimensions: 3,175 L x 18,415 H x 24,13 P (centimètres) 3,175 centimètres de largeur par 18,415 centimètres de hauteur par 24,13 centimètres de profondeur
• Poids: 794 grammes
• Catalogues du vendeur:
  • Reinforcement learning
  • Data science
  • Machine Learning

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind.

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