Decentralized Autonomous Traffic Management through Corridor Networks
As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numbers of crewed and uncrewed aircraft. Dedicated Advanced Air Mobility (AAM) corridors have therefore been proposed for organizing high-density autonomous traffic flows. The desire to scalably provide autonomous aircraft flexibility in trajectory planning motivates the development of decentralized approaches to traffic management in AAM corridors. In this work, we extend a multi-agent reinforcement learning (MARL) approach to address the challenge of decentralized traffic flow management in air corridor networks. We test policies trained in a ...
| Authors | Jasmine Jerry Aloor, Aadarsh Govada, Hamsa Balakrishnan |
|---|---|
| Primary category | cs.MA |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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