Book Image

Python Reinforcement Learning Projects

By : Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani
Book Image

Python Reinforcement Learning Projects

By: Sean Saito, Yang Wenzhuo, Rajalingappaa 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 (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

OpenAI Gym


OpenAI is a non-profit organization dedicated to researching artificial intelligence. Visit https://openai.com for more information about the mission of OpenAI. The technologies developed by OpenAI are free for anyone to use.

Gym

Gym provides a toolkit to benchmark AI-based tasks. The interface is easy to use. The goal is to enable reproducible research. Visit https://gym.openai.com for more information about Gym. An agent can be taught inside of the gym, and learn activities such as playing games or walking. An environment is a library of problems.

The standard set of problems presented in the gym are as follows:

  • CartPole
  • Pendulum
  • Space Invaders
  • Lunar Lander
  • Ant
  • Mountain Car
  • Acrobot
  • Car Racing
  • Bipedal Walker

Any algorithm can work out in the gym by training for these activities. All of the problems have the same interface. Therefore, any general reinforcement learning algorithm can be used through the interface.

Installation 

The primary interface of the gym is used through Python. Once you...