Book Image

Hands-On Intelligent Agents with OpenAI Gym

By : Palanisamy P
Book Image

Hands-On Intelligent Agents with OpenAI Gym

By: Palanisamy P

Overview of this book

Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
Table of Contents (12 chapters)

Summary

In this chapter, we looked at several interesting and valuable learning environments, saw how their interfaces are set up, and even got hands-on with those environments using the quickstart guides for each environment and the setup scripts available in the book's code repository. We first looked at environments that have interfaces compatible with the OpenAI Gym interface that we are now very familiar with. Specifically in this category, we explored the Roboschool and Gym Retro environments.

We also looked at other useful learning environments that did not necessarily have a Gym-compatible environment interface, but had a very similar API and so it was easy to adapt our agent code or implement a wrapper around the learning environment to make it compatible with the Gym API. Specifically, we explored the famous real-time strategy game-based StarCraft II environment...