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

The Bellman Equation and Dynamic Programming

In the previous chapter, we learned that in reinforcement learning our goal is to find the optimal policy. The optimal policy is the policy that selects the correct action in each state so that the agent can get the maximum return and achieve its goal. In this chapter, we'll learn about two interesting classic reinforcement learning algorithms called the value and policy iteration methods, which we can use to find the optimal policy.

Before diving into the value and policy iteration methods directly, first, we will learn about the Bellman equation. The Bellman equation is ubiquitous in reinforcement learning and it is used for finding the optimal value and Q functions. We will understand what the Bellman equation is and how it finds the optimal value and Q functions.

After understanding the Bellman equation, we will learn about two interesting dynamic programming methods called value and policy iterations, which use...