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

Applications of MAB

So far, we have learned about the MAB problem and how can we solve it using various exploration strategies. But our goal is not to just use these algorithms for playing slot machines. We can apply the various exploration strategies to several different use cases.

For instance, bandits can be used as an alternative to AB testing. AB testing is one of the most commonly used classic methods of testing. Say we have two versions of the landing page of our website. Suppose we want to know which version of the landing page is most liked by the users. In this case, we conduct AB testing to understand which version of the landing page is most liked by the users. So, we show version 1 of the landing page to a particular set of users and version 2 of the landing page to other set of users. Then we measure several metrics, such as click-through rate, average time spent on the website, and so on, to understand which version of the landing page is most liked by the users...