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

Distributional Reinforcement Learning

In this chapter, we will learn about distributional reinforcement learning. We will begin the chapter by understanding what exactly distributional reinforcement learning is and why it is useful. Next, we will learn about one of the most popular distributional reinforcement learning algorithms called categorical DQN. We will understand what a categorical DQN is and how it differs from the DQN we learned in Chapter 9, Deep Q Networks and Its Variants, and then we will explore the categorical DQN algorithm in detail.

Following this, we will learn another interesting algorithm called Quantile Regression DQN (QR-DQN). We will understand what a QR-DQN is and how it differs from a categorical DQN, and then we will explore the QR-DQN algorithm in detail.

At the end of the chapter, we will learn about the policy gradient algorithm called the Distributed Distributional Deep Deterministic Policy Gradient (D4PG). We will learn what the D4PG...