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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

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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

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Index

Introduction to the Minecraft environment

The original OpenAI Gym does not contain the Minecraft environment. We need to install a Minecraft environment bundle, available at https://github.com/tambetm/gym-minecraft. This bundle is built based on Microsoft's Malmö, which is a platform for AI experimentation and research built on top of Minecraft.

Before installing the gym-minecraft package, Malmö should first be downloaded from https://github.com/Microsoft/malmo. We can download the latest pre-built version from https://github.com/Microsoft/malmo/releases. After unzipping the package, go to the Minecraft folder and run launchClient.bat on Windows, or launchClient.sh on Linux/MacOS, to launch a Minecraft environment. If it is successfully launched, we can now install gym-minecraft via the following scripts:

python3 -m pip install gym
python3 -m pip install pygame

git clone https://github.com/tambetm/minecraft-py.git
cd minecraft-py
python setup.py install

git clone https://github.com/tambetm...

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

Table of Contents (27) Chapters

Introduction to the Minecraft environment

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Authors (4)

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