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

Playing Atari games using DQN

The Atari 2600 is a popular video game console from a game company called Atari. The Atari game console provides several popular games, such as Pong, Space Invaders, Ms. Pac-Man, Breakout, Centipede, and many more. In this section, we will learn how to build a DQN for playing the Atari games. First, let's explore the architecture of the DQN for playing the Atari games.

Architecture of the DQN

In the Atari environment, the image of the game screen is the state of the environment. So, we just feed the image of the game screen as input to the DQN and it returns the Q values of all the actions in the state. Since we are dealing with images, instead of using a vanilla deep neural network for approximating the Q value, we can use a convolutional neural network (CNN) since it is very effective for handling images.

Thus, now our DQN is a CNN. We feed the image of the game screen (the game state) as input to the CNN, and it outputs the Q...