AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility

TL;DR

AAM-Gym is a new simulation testbed designed to develop and validate AI algorithms for Advanced Air Mobility, enabling researchers to evaluate AI performance in realistic AAM scenarios efficiently.

Contribution

This paper introduces AAM-Gym, the first comprehensive simulation platform for AI development and validation in AAM, supporting diverse use-cases and facilitating cross-sector research.

Findings

Reinforcement learning algorithms tested on AAM-Gym show promising results.

AAM-Gym effectively evaluates AI performance using multiple metrics.

The testbed supports rapid development and validation of AI in AAM contexts.

Abstract

We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of AI algorithms require representative AAM scenarios, as well as a fast time simulation testbed to evaluate their performance. Until now, there has been no such testbed available for AAM to enable a common research platform for individuals in government, industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address this gap by providing an ecosystem to develop, train, and validate new and established AI algorithms across a wide variety of AAM use-cases. In this paper, we use AAM-Gym to study the performance of two reinforcement learning algorithms on an AAM use-case, separation assurance in AAM corridors. The performance of the two algorithms is demonstrated based on a series of metrics provided by AAM-Gym, showing the testbed's utility to AAM research.