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

Introduction to control tasks


OpenAI Gym offers classic control tasks from the classic reinforcement learning literature. These tasks include CartPole, MountainCar, Acrobot, and Pendulum. To find out more, visit the OpenAI Gym website at: https://gym.openai.com/envs/#classic_control. Besides this, Gym also provides more complex continuous control tasks running in the popular physics simulator MuJoCo. Here is the homepage for MuJoCo: http://www.mujoco.org/. MuJoCo stands for Multi-Joint Dynamics with Contact, which is a physics engine for research and development in robotics, graphics, and animation. The tasks provided by Gym are Ant, HalfCheetah, Hopper, Humanoid, InvertedPendulum, Reacher, Swimmer, and Walker2d. These names are very tricky, aren't they? For more details about these tasks, please visit the following link:https://gym.openai.com/envs/#mujoco.

Getting started

If you don't have a full installation of OpenAI Gym, you can install the classic_control and mujoco environment dependencies...