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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

Solving multi-armed bandit problems with the epsilon-greedy policy

Instead of exploring solely with random policy, we can do better with a combination of exploration and exploitation. Here comes the well-known epsilon-greedy policy.

Epsilon-greedy for multi-armed bandits exploits the best action the majority of the time and also keeps exploring different actions from time to time. Given a parameter, ε, with a value from 0 to 1, the probabilities of performing exploration and exploitation are ε and 1 - ε, respectively:

  • Epsilon: Each action is taken with a probability calculated as follows:

Here, |A| is the number of possible actions.

  • Greedy: The action with the highest state-action value is favored, and its probability of being chosen is increased by 1 - ε:
...
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