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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

Setting up the Cliff Walking environment playground

In the first recipe, we will start by getting familiar with the Cliff Walking environment, which we will solve with TD methods in upcoming recipes.

Cliff Walking is a typical gym environment, with long episodes without a guarantee of termination. It is a grid problem with a 4 * 12 board. An agent makes a move up, right, down, and left at a step. The bottom-left tile is the starting point for the agent, and the bottom-right is the winning point where an episode will end if it is reached. The remaining tiles in the last row are cliffs where the agent will be reset to the starting position after stepping on any of them, but the episode continues. Each step the agent takes incurs a -1 reward, with the exception of stepping on the cliffs, where a -100 reward is incurred.

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