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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| Hauptverfasser: | , , , |
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| Format: | E-Book Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton
CRC Press
2010
Taylor & Francis Group |
| Ausgabe: | 1 |
| Schriftenreihe: | Automation and control engineering |
| Schlagworte: | |
| ISBN: | 1439821089, 9781439821084 |
| Online-Zugang: | Volltext |
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Inhaltsangabe:
- 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