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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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Developing the REINFORCE algorithm with baseline

In the REINFORCE algorithm, Monte Carlo plays out the whole trajectory in an episode that is used to update the policy afterward. However, the stochastic policy may take different actions at the same state in different episodes. This can confuse the training, since one sampled experience wants to increase the probability of choosing one action while another sampled experience may want to decrease it. To reduce this high variance problem in vanilla REINFORCE, we will develop a variation algorithm, REINFORCE with baseline, in this recipe.

In REINFORCE with baseline, we subtract the baseline state-value from the return, G. As a result, we use an advantage function A in the gradient update, which is described as follows:

Here, V(s) is the value function that estimates the state-value given a state. Typically, we can use a linear function...

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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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