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

The basic idea of RL

Let's begin with an analogy. Let's suppose we are teaching a dog (agent) to catch a ball. Instead of teaching the dog explicitly to catch a ball, we just throw a ball and every time the dog catches the ball, we give the dog a cookie (reward). If the dog fails to catch the ball, then we do not give it a cookie. So, the dog will figure out what action caused it to receive a cookie and repeat that action. Thus, the dog will understand that catching the ball caused it to receive a cookie and will attempt to repeat catching the ball. Thus, in this way, the dog will learn to catch a ball while aiming to maximize the cookies it can receive.

Similarly, in an RL setting, we will not teach the agent what to do or how to do it; instead, we will give a reward to the agent for every action it does. We will give a positive reward to the agent when it performs a good action and we will give a negative reward to the agent when it performs a bad action. The agent...