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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

Playing Blackjack with Monte Carlo prediction

In this recipe, we will play Blackjack (also called 21) and evaluate a policy we think might work well. You will get more familiar with Monte Carlo prediction with the Blackjack example, and get ready to search for the optimal policy using Monte Carlo control in the upcoming recipes.

Blackjack is a popular card game where the goal is to have the sum of cards as close to 21 as possible without exceeding it. The J, K, and Q cards have a points value of 10, and cards from 2 to 10 have values from 2 to 10. The ace card can be either 1 or 11 points; when the latter value is chosen, it is called a usable ace. The player competes against a dealer. At the beginning, both parties are given two random cards, but only one of the dealer's cards is revealed to the player. The player can request additional cards (called hit) or stop receiving...

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