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

Deep Q-learning


Here comes the fun part—the brain design of our AI Atari player. The core algorithm is based on deep reinforcement learning or deep RL. In order to understand it better, some basic mathematical formulations are required. Deep RL is a perfect combination of deep learning and traditional reinforcement learning. Without understanding the basic concepts about reinforcement learning, it is difficult to apply deep RL correctly in real applications, for example, it is possible that someone may try to use deep RL without defining state space, reward, and transition properly.

Well, don't be afraid of the difficulty of the formulations. We only need high school-level mathematics, and will not go deep into the mathematical proofs of why traditional reinforcement learning algorithms work. The goal of this chapter is to learn the basic Q-learning algorithm, to know how to extend it into the deep Q-learning algorithm (DQN), and to understand the intuition behind these algorithms. Besides...