You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (22) Chapters

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

3. The Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

5. Understanding Temporal Difference Learning

6. Case Study – The MAB Problem

7. Deep Learning Foundations

8. A Primer on TensorFlow

9. Deep Q Network and Its Variants

10. Policy Gradient Method

11. Actor-Critic Methods – A2C and A3C

12. Learning DDPG, TD3, and SAC

13. TRPO, PPO, and ACKTR Methods

14. Distributional Reinforcement Learning

15. Imitation Learning and Inverse RL

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

18. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

Case Study – The MAB Problem

So far in the previous chapters, we have learned the fundamental concepts of reinforcement learning and also several interesting reinforcement learning algorithms. We learned about a model-based method called dynamic programming and a model-free method called the Monte Carlo method, and then we learned about the temporal difference method, which combines the advantages of dynamic programming and the Monte Carlo method.

In this chapter, we will learn about one of the classic problems in reinforcement learning called the multi-armed bandit (MAB) problem. We start the chapter by understanding the MAB problem, and then we will learn about several exploration strategies, called epsilon-greedy, softmax exploration, upper confidence bound, and Thompson sampling, for solving the MAB problem. Following this, we will learn how a MAB is useful in real-world use cases.

Moving forward, we will understand how to find the best advertisement banner...