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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo , Shanmugamani
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Python Reinforcement Learning Projects

Python Reinforcement Learning Projects

5 (1)
By: Sean Saito, Yang Wenzhuo , Shanmugamani

Overview of this book

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
Table of Contents (12 chapters)
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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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