Reinforcement Learning : State-of-the-Art
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement l...
Gespeichert in:
| Hauptverfasser: | , |
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| Weitere Verfasser: | |
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2012.
Cham : Springer Nature |
| Ausgabe: | 1st ed. 2012. |
| Schriftenreihe: | Adaptation, Learning, and Optimization
12 |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Anmerkung: |
Numérisation de l'édition de Springer La pagination de l'édition imprimée correspondante est de : XXXIV-638 p. lectorat : scientifique Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Reinforcement learning, state-of-the-art, Marco Wiering and Martijn van Otterlo (eds.), Heidelberg, Springer, 2012, 1 volume (xxxiv-638 pages), Adaptation, learning, and optimization, 978-3-642-27644-6 • Reinforcement Learning, Texte imprimé, 9783642446856 • Reinforcement Learning, Texte imprimé, 9783642276460 |
Inhaltsangabe:
- Continous State and Action Spaces Relational and First-Order Knowledge Representation Hierarchical Approaches Predictive Approaches Multi-Agent Reinforcement Learning Partially Observable Markov Decision Processes (POMDPs) Decentralized POMDPs (DEC-POMDPs) Features and Function Approximation RL as Supervised Learning (or batch learning) Bounds and complexity RL for Games RL in Robotics Policy Gradient Techniques Least Squares Value Iteration Models and Model Induction Model-based RL Transfer Learning in RL Using of and extracting Knowledge in RL Biological or Psychological Background Evolutionary Approaches Closing chapter, prospects, future issues