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

Quantile Regression DQN

In this section, we will look into another interesting distributional RL algorithm called QR-DQN. It is a distributional DQN algorithm similar to the categorical DQN; however, it has several features that make it more advantageous than a categorical DQN.

Math essentials

Before going ahead, let's recap two important concepts that we use in QR-DQN:

Quantile
Inverse cumulative distribution function (Inverse CDF)

Quantile

When we divide our distribution into equal areas of probability, they are called quantiles. For instance, as Figure 14.18 shows, we have divided our distribution into two equal areas of probabilities and we have two quantiles with 50% probability each:

Figure 14.18: 2-quantile plot

Inverse CDF (quantile function)

To understand an inverse cumulative distribution function (inverse CDF), first, let's learn what a cumulative distribution function (CDF) is.

Consider a random variable...