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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

Solving the coin-flipping gamble problem

Gambling on coin flipping should sound familiar to everyone. In each round of the game, the gambler can make a bet on whether a coin flip will show heads. If it turns out heads, the gambler will win the same amount they bet; otherwise, they will lose this amount. The game continues until the gambler loses (ends up with nothing) or wins (wins more than 100 dollars, let's say). Let's say the coin is unfair and it lands on heads 40% of the time. In order to maximize the chance of winning, how much should the gambler bet based on their current capital in each round? This will definitely be an interesting problem to solve.

If the coin lands on heads more than 50% of the time, there is nothing to discuss. The gambler can just keep betting one dollar each round and should win the game most of the time. If it is a fair coin, the gambler...

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