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

Proximal policy optimization

In the previous section, we learned how TRPO works. We learned that TRPO keeps the policy updates in the trust region by imposing a constraint that the KL divergence between the old and new policy should be less than or equal to . The problem with the TRPO method is that it is difficult to implement and is computationally expensive. So, now we will learn one of the most popular and state-of-the-art policy gradient algorithms called Proximal Policy Optimization (PPO).

PPO improves upon the TRPO algorithm and is simple to implement. Similar to TRPO, PPO ensures that the policy updates are in the trust region. But unlike TRPO, PPO does not use any constraints in the objective function. Going forward, we will learn how exactly PPO works and how PPO ensures that the policy updates are in the trust region.

There are two different types of PPO algorithm: