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Python Reinforcement Learning

You're reading from Python Reinforcement Learning Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

Product type Course

Published in Apr 2019

Publisher Packt

ISBN-13 9781838649777

Length 496 pages

Edition 1st Edition

Languages

Python

Tools

OpenAI Gym

Concepts

Reinforcement Learning

Authors (4):

Yang Wenzhuo

Sean Saito

Sudharsan Ravichandiran

Rajalingappaa Shanmugamani

View More author details

Table of Contents (27) Chapters

Title Page

About Packt

Contributors

Preface

1. Introduction to Reinforcement Learning FREE CHAPTER

2. Getting Started with OpenAI and TensorFlow

3. The Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Playing Atari Games

8. Atari Games with Deep Q Network

9. Playing Doom with a Deep Recurrent Q Network

10. The Asynchronous Advantage Actor Critic Network

11. Policy Gradients and Optimization

12. Balancing CartPole

13. Simulating Control Tasks

14. Building Virtual Worlds in Minecraft

15. Learning to Play Go

16. Creating a Chatbot

17. Generating a Deep Learning Image Classifier

18. Predicting Future Stock Prices

19. Capstone Project - Car Racing Using DQN

20. Looking Ahead

1. Assessments

2. Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

Chapter 2: Getting Started with OpenAI and TensorFlow

conda create --name universe python=3.6 anaconda
With Docker, we can pack our application with its dependencies, which is called container, and we can simply run our applications on the server without using any external dependency with our packed Docker container.
gym.make(env_name)
from gym import envsprint(envs.registry.all())
OpenAI Universe is an extension of OpenAI Gym and it also provides various rich environments.

Placeholder is used for feeding external data whereas variable is used for holding values.
Everything in TensorFlow will be represented as a computational graph that consists of nodes and edges, where nodes are the mathematical operations, say addition, multiplication and so on, and edges are the tensors.
Computation graphs will only be defined; in order to execute the computation graph, we use TensorFlow sessions.

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Authors (4)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

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Yang Wenzhuo

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

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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Sean Saito

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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