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

Summary

We started off the chapter by understanding what the MAB problem is and how it can be solved using several exploration strategies. We first learned about the epsilon-greedy method, where we select a random arm with a probability epsilon and select the best arm with a probability 1-epsilon. Next, we learned about the softmax exploration method, where we select the arm based on the probability distribution, and the probability of each arm is proportional to the average reward.

Following this, we learned about the UCB algorithm, where we select the arm that has the highest upper confidence bound. Then, we explored the Thomspon sampling method, where we learned the distributions of the arms based on the beta distribution.

Moving forward, we learned how MAB can be used as an alternative to AB testing and how can we find the best advertisement banner by framing the problem as a MAB problem. At the end of the chapter, we also had an overview of contextual bandits.

In...