Skip to Main Content
Hands-On Reinforcement Learning with Python
book

Hands-On Reinforcement Learning with Python

by Sudharsan Ravichandiran
June 2018
Intermediate to advanced content levelIntermediate to advanced
318 pages
9h 24m
English
Packt Publishing
Content preview from Hands-On Reinforcement Learning with Python

Building an agent to play Atari games

Now we will see how to build an agent to play any Atari game. You can get the complete code as a Jupyter notebook with the explanation here (https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/blob/master/08.%20Atari%20Games%20with%20DQN/8.8%20Building%20an%20Agent%20to%20Play%20Atari%20Games.ipynb).

First, we import all the necessary libraries:

import numpy as npimport gymimport tensorflow as tffrom tensorflow.contrib.layers import flatten, conv2d, fully_connectedfrom collections import deque, Counterimport randomfrom datetime import datetime

We can use any of the Atari gaming environments given here: http://gym.openai.com/envs/#atari.

In this example, we use the Pac-Man game ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Advanced Deep Learning with Python

Advanced Deep Learning with Python

Ivan Vasilev

Publisher Resources

ISBN: 9781788836524Supplemental Content