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Python Reinforcement Learning Projects

You're reading from Python Reinforcement Learning Projects

Product type Book

Published in Sep 2018

Publisher Packt

ISBN-13 9781788991612

Pages 296 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Authors (3):

Sean Saito

Yang Wenzhuo

Rajalingappaa Shanmugamani

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Up and Running with Reinforcement Learning

2. Balancing CartPole

3. Playing Atari Games

4. Simulating Control Tasks

5. Building Virtual Worlds in Minecraft

6. Learning to Play Go

7. Creating a Chatbot

8. Generating a Deep Learning Image Classifier

9. Predicting Future Stock Prices

10. Looking Ahead

1. Other Books You May Enjoy

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Index

Trust region policy optimization

The trust region policy optimization (TRPO) algorithm was proposed to solve complex continuous control tasks in the following paper: Schulman, S. Levine, P. Moritz, M. Jordan and P. Abbeel. Trust Region Policy Optimization. In ICML, 2015.

To understand why TRPO works requires some mathematical background. The main idea is that it is better to guarantee that the new policy,

, optimized by one training step, not only monotonically decreases the optimization loss function (and thus improves the policy), but also does not deviate from the previous policy

much, which means that there should be a constraint on the difference between

and

, for example,

for a certain constraint function

constant

.

Theory behind TRPO

Let's see the mechanism behind TRPO. If you feel that this part is hard to understand, you can skip it and go directly to how to run TRPO to solve MuJoCo control tasks. Consider an infinite-horizon discounted Markov decision process denoted by

, where...

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Authors (3)

Sean Saito

Sean Saito is the youngest ever Machine Learning Developer at SAP and the first bachelor hired for the position. He currently researches and develops machine learning algorithms that automate financial processes. He graduated from Yale-NUS College in 2017 with a Bachelor of Science degree (with Honours), where he explored unsupervised feature extraction for his thesis. Having a profound interest in hackathons, Sean represented Singapore during Data Science Game 2016, the largest student data science competition. Before attending university in Singapore, Sean grew up in Tokyo, Los Angeles, and Boston.

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See other products by Sean Saito

Yang Wenzhuo

Yang Wenzhuo works as a Data Scientist at SAP, Singapore. He got a bachelor's degree in computer science from Zhejiang University in 2011 and a Ph.D. in machine learning from the National University of Singapore in 2016. His research focuses on optimization in machine learning and deep reinforcement learning. He has published papers on top machine learning/computer vision conferences including ICML and CVPR, and operations research journals including Mathematical Programming.

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Rajalingappaa Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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