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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | Adaptation, Learning, and Optimization,
12 |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.