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

Actor-Critic Methods – A2C and A3C

So far, we have covered two types of methods for learning the optimal policy. One is the value-based method, and the other is the policy-based method. In the value-based method, we use the Q function to extract the optimal policy. In the policy-based method, we compute the optimal policy without using the Q function.

In this chapter, we will learn about another interesting method called the actor-critic method for finding the optimal policy. The actor-critic method makes use of both the value-based and policy-based methods. We will begin the chapter by understanding what the actor-critic method is and how it makes use of value-based and policy-based methods. We will acquire a basic understanding of actor-critic methods, and then we will learn about them in detail.

Moving on, we will also learn how actor-critic differs from the policy gradient with baseline method, and we will learn the algorithm of the actor-critic method in...