Reinforcement Learning: State-of-the-Art - Tapa blanda

Sinopsis

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.

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