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

TD learning

The TD learning algorithm was introduced by Richard S. Sutton in 1988. In the introduction of the chapter, we learned that the reason the TD method became popular is that it combines the advantages of DP and the MC method. But what are those advantages?

First, let's recap quickly the advantages and disadvantages of DP and the MC method.

Dynamic programming—The advantage of the DP method is that it uses the Bellman equation to compute the value of a state. That is, we have learned that according to the Bellman equation, the value of a state can be obtained as the sum of the immediate reward and the discounted value of the next state. This is called bootstrapping. That is, to compute the value of a state, we don't have to wait till the end of the episode, instead, using the Bellman equation, we can estimate the value of a state just based on the value of the next state, and this is called bootstrapping.

Remember how we estimated the...