Lucian Busoniu, Robert Babuska, Bart De Schutter

Reinforcement Learning and Dynamic Programming Using Function Approximators

• Gebundenes Buch

Produktbeschreibung

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 continuous-variable problems, this unparalleled work provides an introduction to classical RL and DP, followed by a presentation of current methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, it offers illustrative examples that readers will be able to adapt to their own work.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 280
• Erscheinungstermin: 29. April 2010
• Englisch
• Abmessung: 241mm x 161mm x 25mm
• Gewicht: 560g
• ISBN-13: 9781439821084
• ISBN-10: 1439821089
• Artikelnr.: 29946521

Autorenporträt

Robert Babuska, Lucian Busoniu, and Bart de Schutter are with the Delft University of Technology. Damien Ernst is with the University of Liege.

Inhaltsangabe

Introduction. Dynamic programming and reinforcement learning. Focus of this
book. Book outline. Basics of dynamic programming and reinforcement
learning. Introduction. Markov decision processes. Value iteration. Policy
iteration. Direct policy search. Conclusions. Bibliographical notes.
Dynamic programming and reinforcement learning in large and continuous
spaces. Introduction. The need for approximation in large and continuous
spaces. Approximate value iteration. Approximate policy iteration. Finding
value function approximators automatically. Approximate policy search.
Comparison of approximate value iteration, policy iteration, and policy
search. Conclusions. Bibliographical notes. Q-value iteration with fuzzy
approximation. Introduction. Fuzzy Q-iteration. Analysis of fuzzy
Q-iteration. Optimizing the membership functions. Experimental studies.
Conclusions. Bibliographical notes. Online and continuous-action
least-squares policy iteration. Introduction. Least-squares policy
iteration. LSPI with continuous-action approximation. Online LSPI. Using
prior knowledge in online LSPI. Experimental studies. Conclusions.
Bibliographical notes. Direct policy search with adaptive basis functions.
Introduction. Policy search with adaptive basis functions. Experimental
studies. Conclusions. Bibliographical notes. References. Glossary.