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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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Playing CartPole through the cross-entropy method

In this last recipe, by way of a bonus (and fun) section, we will develop a simple, yet powerful, algorithm to solve CartPole. It is based on cross-entropy, and directly maps input states to an output action. In fact, it is more straightforward than all the other policy gradient algorithms in this chapter.

We have applied several policy gradient algorithms to solve the CartPole environment. They use complicated neural network architectures and a loss function, which may be overkill for simple environments such as CartPole. Why don't we directly predict the actions for given states? The idea behind this is straightforward: we model the mapping from state to action, and train it ONLY with the most successful experiences from the past. We are only interested in what the correct actions should be. The objective function, in this...

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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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