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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

Scaling Up Learning with Function Approximation

So far, we have represented the value function in the form of a lookup table in the MC and TD methods. The TD method is able to update the Q-function on the fly during an episode, which is considered an advancement on the MC method. However, the TD method is still not sufficiently scalable for problems with many states and/or actions. It will be extremely slow at learning too many values for individual pairs of states and actions using the TD method.

This chapter will focus on function approximation, which can overcome the scaling issues in the TD method. We will begin by setting up the Mountain Car environment playground. After developing the linear function estimator, we will incorporate it into the Q-learning and SARSA algorithms. We will then improve the Q-learning algorithm using experience replay, and experiment with using...

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