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

Understanding Temporal Difference Learning

Temporal difference (TD) learning is one of the most popular and widely used model-free methods. The reason for this is that TD learning combines the advantages of both the dynamic programming (DP) method and the Monte Carlo (MC) method we covered in the previous chapters.

We will begin the chapter by understanding how exactly TD learning is beneficial compared to DP and MC methods. Later, we will learn how to perform the prediction task using TD learning. Going forward, we will learn how to perform TD control tasks with an on-policy TD control method called SARSA and an off-policy TD control method called Q learning.

We will also learn how to find the optimal policy in the Frozen Lake environment using SARSA and the Q learning method. At the end of the chapter, we will compare the DP, MC, and TD methods.

Thus, in this chapter, we will learn about the following topics: