A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researchers have greatly advanced algorithms for learning and acting in MDPs. This book reviews such algorithms, beginning with well-known dynamic programming methods for solving MDPs such as policy iteration and value iteration, then describes approximate dynamic programming methods such as trajectory based value iteration, and finally moves to reinforcement learning methods such as Q-Learning, SARSA, and least-squares policy iteration. It describes algorithms in a unified framework, giving pseudocode…
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Produktdetails
Weitere Autoren: Walsh, Thomas J. / Tellex, Stefanie
- ISBN: 978-1-60198-760-0
- EAN: 9781601987600
- Produktnummer: 15815659
- Verlag: Now Publishers Inc
- Sprache: Englisch
- Erscheinungsjahr: 2013
- Seitenangabe: 92 S.
- Masse: H23.4 cm x B15.6 cm x D0.5 cm 155 g
- Abbildungen: Paperback
- Gewicht: 155
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