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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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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

Solving the CartPole problem with function approximation

This is a bonus recipe in this chapter, where we will solve the CartPole problem using FA.

As we saw in Chapter 1, Getting started with reinforcement learning and PyTorch, we simulated the CartPole environment in the , Simulating the CartPole environment recipe, and solved the environment using random search, and the hill climbing and policy gradient algorithms, respectively, in recipes including Implementing and evaluating the random search policy, Developing the hill climbing algorithm, and Developing the policy gradient algorithm. Now, let's try to solve CartPole using what we've talked about in this chapter.

How to do it...

We demonstrate the solution...

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