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

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

Chapter 14 – Distributional Reinforcement Learning

In a distributional RL, instead of selecting an action based on the expected return, we select the action based on the distribution of the return, which is often called the value distribution or return distribution.
In categorical DQN, we feed the state and support of the distribution as the input and the network returns the probabilities of the value distribution.
The authors of the categorical DQN suggest that it will be efficient to choose the number of support N as 51 and so the categorical DQN is also known as the C51 algorithm.
Inverse CDF is also known as the quantile function. Inverse CDF as the name suggests is the inverse of the cumulative distribution function. That is, in CDF, given the support x, we obtain the cumulative probability , whereas in inverse CDF, given cumulative probability , we obtain the support x.
In a categorical DQN, along with the state, we feed the fixed support...