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

Distributed Distributional DDPG

D4PG, which stands for Distributed Distributional Deep Deterministic Policy Gradient, is one of the most interesting policy gradient algorithms. We can make a guess about how D4PG works just by its name. As the name suggests, D4PG is basically a combination of deep deterministic policy gradient (DDPG) and distributional reinforcement learning, and it works in a distributed fashion. Confused? Let's go deeper and understand how D4PG works in detail.

To understand how D4PG works, it is highly recommended to revise the DDPG algorithm we covered in Chapter 12, Learning DDPG, TD3, and SAC. We learned that DDPG is an actor critic method where the actor tries to learn the policy while the critic tries to evaluate the policy produced by the actor using the Q function. The critic uses the deep Q network for estimating the Q function and the actor uses the policy network for computing the policy. Thus, the actor performs an action while the critic gives...