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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 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

Setting up the continuous Mountain Car environment

So far, the environments we have worked on have discrete action values, such as 0 or 1, representing up or down, left or right. In this recipe, we will experience a Mountain Car environment with continuous actions.

Continuous Mountain Car (https://github.com/openai/gym/wiki/MountainCarContinuous-v0) is a Mountain Car environment with continuous actions whose value is from -1 to 1. As shown in the following screenshot, its goal is to get the car to the top of the hill on the right-hand side:

In a one-dimensional track, the car is positioned between -1.2 (leftmost) and 0.6 (rightmost), and the goal (yellow flag) is located at 0.5. The engine of the car is not strong enough to drive it to the top in a single pass, so it has to drive back and forth to build up momentum. Hence, the action is a float that represents the force of pushing...

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