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...
Συγγραφή απο Οργανισμό/Αρχή: | |
---|---|
Άλλοι συγγραφείς: | , |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2012.
|
Σειρά: | Adaptation, Learning, and Optimization,
12 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.