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

Summary


This chapter introduced the classical control tasks and the MuJoCo control tasks provided by Gym. You have learned the goals and specifications of these tasks and how to implement a simulator for them. The most important parts of this chapter were the deterministic DPG and the TRPO for continuous control tasks. You learned the theory behind them, which explains why they work well in these tasks. You also learned how to implement DPG and TRPO using TensorFlow, and how to visualize the training procedure.

In the next chapter, we will learn about how to apply reinforcement learning algorithms to more complex tasks, for example, playing Minecraft. We will introduce the Asynchronous Actor-Critic (A3C) algorithm, which is much faster than DQN at complex tasks, and has been widely applied as a framework in many deep reinforcement learning algorithms.