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

by   Chanyoung Park, et al.

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.


page 3

page 5

page 9

page 10

page 11

page 12

page 14

page 15


Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility

It has been considered that urban air mobility (UAM), also known as dron...

FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers Goods Transportation

The growth in online goods delivery is causing a dramatic surge in urban...

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Recently, practical applications for passenger flow prediction have brou...

Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks

The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneo...

Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, ...

Graph Learning Based Decision Support for Multi-Aircraft Take-Off and Landing at Urban Air Mobility Vertiports

Majority of aircraft under the Urban Air Mobility (UAM) concept are expe...

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Autonomous mobility is emerging as a new mode of urban transportation fo...

Please sign up or login with your details

Forgot password? Click here to reset