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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

- Temporal difference learning in finite state spaces
- 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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