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Reinforcement Learning

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Cover of 'Reinforcement Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Reinforcement Learning and Markov Decision Processes
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    Chapter 2 Batch Reinforcement Learning
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    Chapter 3 Least-Squares Methods for Policy Iteration
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    Chapter 4 Learning and Using Models
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    Chapter 5 Transfer in Reinforcement Learning: A Framework and a Survey
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    Chapter 6 Sample Complexity Bounds of Exploration
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    Chapter 7 Reinforcement Learning in Continuous State and Action Spaces
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    Chapter 8 Solving Relational and First-Order Logical Markov Decision Processes: A Survey
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    Chapter 9 Hierarchical Approaches
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    Chapter 10 Evolutionary Computation for Reinforcement Learning
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    Chapter 11 Bayesian Reinforcement Learning
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    Chapter 12 Partially Observable Markov Decision Processes
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    Chapter 13 Predictively Defined Representations of State
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    Chapter 14 Game Theory and Multi-agent Reinforcement Learning
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    Chapter 15 Decentralized POMDPs
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    Chapter 16 Psychological and Neuroscientific Connections with Reinforcement Learning
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    Chapter 17 Reinforcement Learning in Games
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    Chapter 18 Reinforcement Learning in Robotics: A Survey
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    Chapter 19 Conclusions, Future Directions and Outlook
Attention for Chapter 7: Reinforcement Learning in Continuous State and Action Spaces
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