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

Soft actor-critic

Now, we will look into another interesting actor-critic algorithm, called SAC. This is an off-policy algorithm and it borrows several features from the TD3 algorithm. But unlike TD3, it uses a stochastic policy . SAC is based on the concept of entropy. So first, let's understand what is meant by entropy. Entropy is a measure of the randomness of a variable. It basically tells us the uncertainty or unpredictability of the random variable and is denoted by .

If the random variable always gives the same value every time, then we can say that its entropy is low because there is no randomness. But if the random variable gives different values, then we can say that its entropy is high.

For an example, consider a dice throw experiment. Every time a dice is thrown, if we get a different number, then we can say that the entropy is high because we are getting a different number every time and there is high uncertainty since we don't know which number will...