# Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework

arXiv: https://arxiv.org/abs/2508.16440 · HTML: https://mlsanity.com/paper/2508.16440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance](https://mlsanity.com/paper/2003.08353/) (2020)
- [Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction](https://mlsanity.com/paper/2305.10556/) (2023)
- [Noise-Aware and Equitable Urban Air Traffic Management: An Optimization Approach](https://mlsanity.com/paper/2401.00806/) (2024)
- [Self-organized free-flight arrival for urban air mobility](https://mlsanity.com/paper/2404.03710/) (2024)

## Abstract (arXiv)

Urban Air Mobility (UAM) envisions the widespread use of small aerial vehicles to transform transportation in dense urban environments. However, UAM faces critical operational challenges, particularly the balance between minimizing noise exposure and maintaining safe separation in low-altitude urban airspace, two potentially conflicting objectives that are often addressed separately. We propose a reinforcement learning (RL)-based air traffic management system that integrates both noise and safety considerations within a unified, decentralized framework. Under this scalable air traffic coordination solution, agents operate in a structured, multi-layered airspace and learn altitude adjustment policies to jointly manage noise impact and separation constraints. The system demonstrates strong performance across both objectives and reveals tradeoffs among separation, noise exposure, and energy efficiency under high traffic density. Among the three objectives, safe separation is accorded the highest priority, whereas the relative significance of noise and energy varies by location and is contingent upon financial and public policy considerations. The findings highlight the potential of RL and multi-objective coordination strategies in enhancing the safety, quietness, and efficiency of UAM operations.