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

Trust region policy optimization

TRPO is one of the most popularly used algorithms in deep reinforcement learning. TRPO is a policy gradient algorithm and it acts as an improvement to the policy gradient with baseline method we learned in Chapter 10, Policy Gradient Method. We learned that policy gradient is an on-policy method, meaning that on every iteration, we improve the same policy with which we are generating trajectories. On every iteration, we update the parameter of our network and try to find the improved policy. The update rule for updating the parameter of our network is given as follows:

Where is the gradient and is known as the step size or learning rate. If the step size is large then there will be a large policy update, and if it is small then there will be a small update in the policy. How can we find an optimal step size? In the policy gradient method, we keep the step size small and so on every iteration there will be a small improvement in the...