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

Summary

We started off the chapter by understanding the DDPG algorithm. We learned that DDPG is an actor-critic algorithm where the actor estimates the policy using policy gradient and the critic evaluates the policy produced by the actor using the Q function. We learned how DDPG uses a deterministic policy and how it is used in environments with a continuous action space.

Later, we looked into the actor and critic components of DDPG in detail and understood how they work, before finally learning about the DDPG algorithm.

Moving on, we learned about the twin delayed DDPG, which is the successor to DDPG and constitutes an improvement to the DDPG algorithm. We learned the key features of TD3, including clipped double Q learning, delayed policy updates, and target policy smoothing, in detail and finally, we looked into the TD3 algorithm.

At the end of the chapter, we learned about the SAC algorithm. We learned that, unlike DDPG and TD3, the SAC method uses a stochastic policy...