Reinforcement Learning and Dynamic Programming Using Function Approximators

While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on conti...

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Bibliographic Details
Main Authors:	Busoniu, Lucian, Babuska, Robert, De Schutter, Bart, Ernst, Damien
Format:	eBook Book
Language:	English
Published:	Boca Raton CRC Press 2010 Taylor & Francis Group
Edition:	1
Series:	Automation and control engineering
Subjects:	Computer science Digital control systems Dynamic programming Engineering, computing & technology Ingénierie, informatique & technologie Sciences informatiques Approximate Policy Policy Search Parameter Vector Execution Time RL Method Bellman Optimality Equation Reward Function Fuzzy Approximator Inverted Pendulum Continuous Action Control Pi Approximate DP Bellman Equation Inverted Pendulum Problem Policy Improvement Step DC Motor Policy Iteration Algorithms State Action Pair Stochastic Case Triangular MFs Cross-entropy Method Fuzzy Partition RL Algorithm Policy Improvement Deterministic Case
ISBN:	1439821089, 9781439821084
Online Access:	Get full text
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Table of Contents:

Cover -- Title -- Copyright -- Preface -- About the authors -- Contents -- 1 Introduction -- 2 An introduction to dynamic programming and reinforcement learning -- 3 Dynamic programming and reinforcement learning in large and continuous spaces -- 4 Approximate value iteration with a fuzzy representation -- 5 Approximate policy iteration for online learning and continuous-action control -- 6 Approximate policy search with cross-entropy optimization of basis functions -- Appendix A: Extremely randomized trees -- Appendix B: The cross-entropy method -- Symbols and abbreviations -- Bibliography -- List of algorithms -- Index