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

TRPO, PPO, and ACKTR Methods

In this chapter, we will learn two interesting state-of-art policy gradient algorithms: trust region policy optimization and proximal policy optimization. Both of these algorithms act as an improvement to the policy gradient algorithm (REINFORCE with baseline) we learned in Chapter 10, Policy Gradient Method.

We begin the chapter by understanding the Trust Region Policy Optimization (TRPO) method and how it acts as an improvement to the policy gradient method. Later we will understand several essential math concepts that are required to understand TRPO. Following this, we will learn how to design and solve the TRPO objective function. At the end of the section, we will understand how the TRPO algorithm works step by step.

Moving on, we will learn about Proximal Policy Optimization (PPO). We will understand how PPO works and how it acts as an improvement to the TRPO algorithm in detail. We will also learn two types of PPO algorithm called PPO...