Reinforcement learning

an introduction Adaptive computation and machine learning

322 pages

English language

Published Jan. 24, 1998 by MIT Press.

View on OpenLibrary

3 stars (1 review)

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.

1 edition

Subjects

  • Reinforcement learning (Machine learning)