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PyTorch 1.x Reinforcement Learning Cookbook

You're reading from PyTorch 1.x Reinforcement Learning Cookbook Over 60 recipes to design, develop, and deploy self-learning AI models using Python

Product type Paperback

Published in Oct 2019

Publisher Packt

ISBN-13 9781838551964

Length 340 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Reinforcement Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (11) Chapters

Preface

1. Getting Started with Reinforcement Learning and PyTorch FREE CHAPTER

2. Markov Decision Processes and Dynamic Programming

3. Monte Carlo Methods for Making Numerical Estimations

4. Temporal Difference and Q-Learning

5. Solving Multi-armed Bandit Problems

6. Scaling Up Learning with Function Approximation

7. Deep Q-Networks in Action

8. Implementing Policy Gradients and Policy Optimization

9. Capstone Project – Playing Flappy Bird with DQN

10. Other Books You May Enjoy

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Setting up the game environment

To play Flappy Bird with a DQN, we first need to set up the environment.

We’ll simulate the Flappy Bird game using Pygame. Pygame (https://www.pygame.org) contains a set of Python modules developed for creating video games. It also includes graphics and sound libraries needed in games. We can install the Pygame package as follows:

pip install pygame

Flappy Bird is a famous mobile game originally developed by Dong Nguyen. You can try it yourself, using your keyboard, at https://flappybird.io/. The aim of the game is to remain alive as long as possible. The game ends when the bird touches the floor or a pipe. So, the bird needs to flap its wings at the right times to get through the random pipes and to avoid falling to the ground. Possible actions include flapping and not flapping. In the game environment, the reward is +0.1 for every step...

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Authors (1)

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.

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