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

Learning DDPG, TD3, and SAC

In the previous chapter, we learned about interesting actor-critic methods, such as Advantage Actor-Critic (A2C) and Asynchronous Advantage Actor-Critic (A3C). In this chapter, we will learn several state-of-the-art actor-critic methods. We will start off the chapter by understanding one of the popular actor-critic methods called Deep Deterministic Policy Gradient (DDPG). DDPG is used only in continuous environments, that is, environments with a continuous action space. We will understand what DDPG is and how it works in detail. We will also learn the DDPG algorithm step by step.

Going forward, we will learn about the Twin Delayed Deep Deterministic Policy Gradient (TD3). TD3 is an improvement over the DDPG algorithm and includes several interesting features that solve the problems faced in DDPG. We will understand the key features of TD3 in detail and also look into the algorithm of TD3 step by step.

Finally, we will learn...