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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 2. Markov Decision Processes and Dynamic Programming FREE CHAPTER 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

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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