Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
This work addresses efficient passenger delivery in UAM systems, which is an incremental improvement for future transportation networks.
The paper tackles the problem of dynamic and uncertain factors in urban air mobility (UAM) networks, such as passenger requests and collisions, by proposing a novel cooperative multi-agent deep reinforcement learning algorithm, which results in a 30% increase in serviced passengers and a 26% decrease in waiting time per passenger.
It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key role in future transportation. By putting UAM into practical future transportation, several benefits can be realized, i.e., (i) the total travel time of passengers can be reduced compared to traditional transportation and (ii) there is no environmental pollution and no special labor costs to operate the system because electric batteries will be used in UAM system. However, there are various dynamic and uncertain factors in the flight environment, i.e., passenger sudden service requests, battery discharge, and collision among UAMs. Therefore, this paper proposes a novel cooperative MADRL algorithm based on centralized training and distributed execution (CTDE) concepts for reliable and efficient passenger delivery in UAM networks. According to the performance evaluation results, we confirm that the proposed algorithm outperforms other existing algorithms in terms of the number of serviced passengers increase (30%) and the waiting time per serviced passenger decrease (26%).