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 what TRPO is and how it acts as an improvement to the policy gradient algorithm. We learned that when the new policy and old policy vary greatly then it causes model collapse.

So in TRPO, we make a policy update while imposing the constraint that the parameters of the old and new policies should stay within the trust region. We also learned that TRPO guarantees monotonic policy improvement; that is, it guarantees that there will always be a policy improvement on every iteration.

Later, we learned about the PPO algorithm, which acts as an improvement to the TRPO algorithm. We learned about two types of PPO algorithm: PPO-clipped and PPO-penalty. In the PPO-clipped method, in order to ensure that the policy updates are in the trust region, PPO adds a new function called the clipping function that ensures the new and old policies are not far away from each other. In the PPO-penalty method, we modify our objective function...