@inproceedings{aa56f507f3844279920067debd784ba6,
title = "Application of an autonomous multi-agent system using Proximal Policy Optimisation for tactical deconfliction within the urban airspace",
abstract = "The present paper formalises the development of a Multi-agent Reinforcement Learning (MARL) solver for U-space Service Providers (USSPs) supporting the tactical conflict resolution and exhibited in the Air Mobility Urban - Large Experimental Demonstration (AMU-LED) project. It relies on an Advantage Actor Critic (A2C) model with a Proximal Policy Optimisation (PPO) learning baseline. The application of the autonomous system is demonstrated under a synthetic (with live and virtual) air/unmanned traffic management (ATM/UTM) environment. The Unmanned Aircraft Systems (UASs) are flying in cruise phase at low altitudes, whose respective flight plan generates intersections for enforcing a high collision frequency. The study adopts a step-wise complexity approach of scenarios that confront two agents' observation methods and showcases a practical case of tactical conflict resolution. The experiments show encouraging deconfliction performance with promising prospects for seeing a such solver deployed.",
keywords = "Actor Critic, Multi-Agent System, Proximal Policy Optimization, Reinforcement Learning, U-space Service Providers, Urban Air Mobility",
author = "Rodolphe Fremond and Yan Xu and Gokhan Inalhan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1109/DASC55683.2022.9925730",
language = "英语",
series = "AIAA/IEEE Digital Avionics Systems Conference - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings",
address = "美国",
}