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

A Primer on TensorFlow

TensorFlow is one of the most popular deep learning libraries. In upcoming chapters, we will use TensorFlow to build deep reinforcement models. So, in this chapter, we will get ourselves familiar with TensorFlow and its functionalities.

We will learn about what computational graphs are and how TensorFlow uses them. We will also explore TensorBoard, which is a visualization tool provided by TensorFlow used for visualizing models. Going forward, we will understand how to build a neural network with TensorFlow to perform handwritten digit classification.

Moving on, we will learn about TensorFlow 2.0, which is the latest version of TensorFlow. We will understand how TensorFlow 2.0 differs from its previous versions and how it uses Keras as its high-level API.

In this chapter, we will learn about the following: