# Decentralized Autonomous Traffic Management through Corridor Networks

arXiv: https://arxiv.org/abs/2606.23585 · HTML: https://mlsanity.com/paper/2606.23585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation](https://mlsanity.com/paper/2211.02127/) (2022)
- [Resolving Conflicting Constraints in Multi-Agent Reinforcement Learning with Layered Safety](https://mlsanity.com/paper/2505.02293/) (2025)
- [Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems](https://mlsanity.com/paper/2507.14850/) (2025)
- [Strategic Hub-Based Platoon Coordination under Uncertain Travel Times](https://mlsanity.com/paper/2104.00255/) (2021)
- [Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks](https://mlsanity.com/paper/2308.04958/) (2023)
- [Adaptive Traffic-Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems](https://mlsanity.com/paper/2506.00703/) (2025)
- [Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination](https://mlsanity.com/paper/2605.20625/) (2026)

## Abstract (arXiv)

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 single-corridor setting on increasingly complex multi-corridor networks with combinations of merges and splits in a zero-shot manner. Experimental results demonstrate that learned behaviors transfer well to scenarios with varying traffic density, network geometry, and heterogeneous vehicle performance, without needing centralized coordination or model retraining. We evaluate system-level performance in terms of conformance to corridor boundaries, completion rates, average speeds, distance traveled, and maintenance of inter-aircraft separation. We find that although our policies require only locally coordinated entry, traversal, and exit behaviors, they collectively produce desirable traffic flows through the corridor network.