# AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility

arXiv: https://arxiv.org/abs/2206.04513 · HTML: https://mlsanity.com/paper/2206.04513/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis](https://mlsanity.com/paper/1807.07814/) (2018)
- [Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach](https://mlsanity.com/paper/1812.11670/) (2018)
- [Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance](https://mlsanity.com/paper/2105.02331/) (2021)
- [Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning](https://mlsanity.com/paper/1912.10146/) (2019)
- [Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers](https://mlsanity.com/paper/2204.14250/) (2022)
- [Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling](https://mlsanity.com/paper/2110.12858/) (2021)

## Built on by (most cited first, 4 of 4)

- [Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks](https://mlsanity.com/paper/2308.04958/) (2023)
- [Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey](https://mlsanity.com/paper/2301.12901/) (2023)
- [Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management](https://mlsanity.com/paper/2311.10805/) (2023)
- [Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility](https://mlsanity.com/paper/2407.00197/) (2024)

## Abstract (arXiv)

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.