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

Categorical DQN

In the last section, we learned why it is more beneficial to choose an action based on the distribution of return than to choose an action based on the Q value, which is just the expected return. In this section, we will understand how to compute the distribution of return using an algorithm called categorical DQN.

The distribution of return is often called the value distribution or return distribution. Let Z be the random variable and Z(s, a) denote the value distribution of a state s and an action a. We know that the Q function is represented by Q(s, a) and it gives the value of a state-action pair. Similarly, now we have Z(s, a) and it gives the value distribution (return distribution) of the state-action pair.

Okay, how can we compute Z(s, a)? First, let's recollect how we compute Q(s, a).

In DQN, we learned that we use a neural network to approximate the Q function, Q(s, a), Since we use a neural network to approximate the Q function, we can represent...