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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

Capstone Project – Playing Flappy Bird with DQN

In this very last chapter, we will work on a capstone project—playing Flappy Bird using reinforcement learning. We will apply what we have learned throughout this book to build an intelligent bot. We will also focus on building Deep Q-Networks (DQNs), fine-tuning model parameters, and deploying the model. Let's see how long the bird can stay in the air.

The capstone project will be built section by section in the following recipes:

  • Setting up the game environment
  • Building a Deep Q-Network to play Flappy Bird
  • Training and tuning the network
  • Deploying the model and playing the game

As a result, the code in each recipe is to be built on top of the previous recipes.

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