mlsanityData to 14 Oct 2026

AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility

arXiv 2206.04513 ↗First posted 9 Jun 2022cs.AI eess.SPcited by 4

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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.

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

14 of 39 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
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  11. 2021Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurancecited by 8
  12. 2019Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learningcited by 6
2 more
  1. 2022Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuverscited by 6
  2. 2021Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Schedulingcited by 1

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networkscited by 4
  2. 2023Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Surveycited by 1
  3. 2023Towards a Standardized Reinforcement Learning Framework for AAM Contingency Managementcited by 1
  4. 2024Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobilitycited by 0