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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

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781838551964
Length 340 pages
Edition 1st Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Toc

Table of Contents (11) Chapters Close

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

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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