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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

Product type Paperback

Published in Oct 2019

Publisher Packt

ISBN-13 9781838551964

Length 340 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Reinforcement Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (11) Chapters

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

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Simulating the FrozenLake environment

The optimal policies for the MDPs we have dealt with so far are pretty intuitive. However, it won't be that straightforward in most cases, such as the FrozenLake environment. In this recipe, let's play around with the FrozenLake environment and get ready for upcoming recipes where we will find its optimal policy.

FrozenLake is a typical Gym environment with a discrete state space. It is about moving an agent from the starting location to the goal location in a grid world, and at the same time avoiding traps. The grid is either four by four (https://gym.openai.com/envs/FrozenLake-v0/) or eight by eigh.

t (https://gym.openai.com/envs/FrozenLake8x8-v0/). The grid is made up of the following four types of tiles:

S: The starting location
G: The goal location, which terminates an episode
F: The frozen tile, which is a walkable location...

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Authors (1)

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.

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