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

Contextual bandits

We just learned how to use bandits to find the best advertisement banner for the users. But the banner preference varies from user to user. That is, user A likes banner 1, but user B might like banner 3, and so on. Each user has their own preferences. So, we have to personalize advertisement banners according to each user. How can we do that? This is where we use contextual bandits.

In the MAB problem, we just perform the action and receive a reward. But with contextual bandits, we take actions based on the state of the environment and the state holds the context.

For instance, in the advertisement banner example, the state specifies the user behavior and we will take action (show the banner) according to the state (user behavior) that will result in the maximum reward (ad clicks).

Contextual bandits are widely used for personalizing content according to the user's behavior. They are also used to solve the cold-start problems faced by recommendation...