# Multi-Agent Reinforcement Learning for Cooperative Air Transportation Services in City-Wide Autonomous Urban Air Mobility

arXiv: https://arxiv.org/abs/2306.04137 · HTML: https://mlsanity.com/paper/2306.04137/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control](https://mlsanity.com/paper/2504.10831/) (2025)
- [Self-organized free-flight arrival for urban air mobility](https://mlsanity.com/paper/2404.03710/) (2024)
- [Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2406.16994/) (2024)
- [An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.02027/) (2025)
- [Cooperative-Competitive Team Play of Real-World Craft Robots](https://mlsanity.com/paper/2602.21119/) (2026)

## Abstract (arXiv)

The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this paper proposes a novel air transportation service management algorithm based on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM cooperation. Specifically, the proposed algorithm in this paper is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. Furthermore, this paper adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. Furthermore, there are no inferior UAMs by utilizing parameter sharing in CommNet and a centralized critic network in CTDE. Therefore, it can be confirmed that the research results in this paper can provide a promising solution for autonomous air transportation management systems in city-wide urban areas.