Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this compr
116 36 19MB
English Pages 290 Year 2023
Table of contents :
Cover
Front Matter
Part I. Foundation
1. Introduction
2. Markov Decision Processes
3. Dynamic Programming
4. Monte Carlo Methods
5. Temporal Difference Learning
Part II. Value Function Approximation
6. Linear Value Function Approximation
7. Nonlinear Value Function Approximation
8. Improvements to DQN
Part III. Policy Approximation
9. Policy Gradient Methods
10. Problems with Continuous Action Space
11. Advanced Policy Gradient Methods
Part IV. Advanced Topics
12. Distributed Reinforcement Learning
13. Curiosity-Driven Exploration
14. Planning with a Model: AlphaZero
Back Matter