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)

What can you do with the OpenAI Gym toolkit?

The Gym toolkit provides a standardized way of defining the interface for environments developed for problems that can be solved using reinforcement learning. If you are familiar with or have heard of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), you may realize how much of an impact a standard benchmarking platform can have on accelerating research and development. For those of you who are not familiar with ILSVRC, here is a brief summary: it is a competition where the participating teams evaluate the supervised learning algorithms they have developed for the given dataset and compete to achieve higher accuracy with several visual recognition tasks. This common platform, coupled with the success of deep neural network-based algorithms popularized by AlexNet (https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf), paved the way for the deep learning era we are in at the moment.

In a similar way, the Gym toolkit provides a common platform to benchmark reinforcement learning algorithms and encourages researchers and engineers to develop algorithms that can achieve higher rewards for several challenging tasks. In short, the Gym toolkit is to reinforcement learning what ILSVRC is to supervised learning.