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

Policy Gradient Method

In the previous chapters, we learned how to use value-based reinforcement learning algorithms to compute the optimal policy. That is, we learned that with value-based methods, we compute the optimal Q function iteratively and from the optimal Q function, we extract the optimal policy. In this chapter, we will learn about policy-based methods, where we can compute the optimal policy without having to compute the optimal Q function.

We will start the chapter by looking at the disadvantages of computing a policy from the Q function, and then we will learn how policy-based methods learn the optimal policy directly without computing the Q function. Next, we will examine one of the most popular policy-based methods, called the policy gradient. We will first take a broad overview of the policy gradient algorithm, and then we will learn more about it in detail.

Going forward, we will also learn how to derive the policy gradient step by step and examine...