Reinforcement Learning State-of-the-Art /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Wiering, Marco (Editor), Otterlo, Martijn van (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
• Series: Adaptation, Learning, and Optimization, 12
• Subjects: Engineering.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).

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...

Table of Contents:

• 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.