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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 Taxi problem with Q-learning

The Taxi problem (https://gym.openai.com/envs/Taxi-v2/) is another popular grid world problem. In a 5 * 5 grid, the agent acts as a taxi driver to pick up a passenger at one location and then drop the passenger off at their destination. Take a look at the following example:

Colored tiles have the following meanings:

  • Yellow: The starting position of the taxi. The starting location is random in each episode.
  • Blue: The position of the passenger. It is also randomly selected in each episode.
  • Purple: The destination of the passenger. Again, it is randomly selected in each episode.
  • Green: The position of the taxi with the passenger.

The four letters R, Y, B, and G indicate the only tiles that allow picking up and dropping off the passenger. One of them is the destination, and one is where the passenger is located.

The taxi can take the following...

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