By Unknown
Book Description
Table of Contents
  • Overview
  • Markov decision processes
    • Preliminaries
    • Markov Decision Processes
    • Value functions
    • Dynamic programming algorithms for solving MDPs
  • Value prediction problems
    • Temporal difference learning in finite state spaces
      • Tabular TD(0)
      • Every-visit Monte-Carlo
      • TD(): Unifying Monte-Carlo and TD(0)
    • Algorithms for large state spaces
      • TD() with function approximation
      • Gradient temporal difference learning
      • Least-squares methods
      • The choice of the function space
  • Control
    • A catalog of learning problems
    • Closed-loop interactive learning
      • Online learning in bandits
      • Active learning in bandits
      • Active learning in Markov Decision Processes
      • Online learning in Markov Decision Processes
    • Direct methods
      • Q-learning in finite MDPs
      • Q-learning with function approximation
    • Actor-critic methods
      • Implementing a critic
      • Implementing an actor
  • For further exploration
    • Further reading
    • Applications
    • Software
    • Acknowledgements
  • The theory of discounted Markovian decision processes
    • Contractions and Banach's fixed-point theorem
    • Application to MDPs
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