Drone taxi system based on multi-agent reinforcement learning and drone taxi operation using the same

A processor-implemented method using a drone taxi system based on multi-agent reinforcement learning, the drone taxi operation method comprising:

• a passenger search operation in which a drone taxi searches for a passenger present within a certain range and receives call information including a departure location point and a destination location point from passenger terminals;
• a passenger selection operation in which a control server receives the call information of passengers from the drone taxi and selects a candidate passenger depending on whether a passenger is present;
• and
• a route setting operation in which the control server generates travel route information of each drone taxi from drone state information of a plurality of drone taxies using an optimization model trained through the multi-agent reinforcement learning, transmits the travel route information to each drone taxi, and control each drone taxi to travel, according to the generated travel route information and under a control of the control server, to a departure location point of the selected candidate passenger, the controlling further including controlling each drone taxi to update a route of the traveling to be set with the generated travel route information and controlling each drone taxi to transmit assignment information and boarding information to a terminal of the selected candidate passenger,
• wherein the control server comprises:
• a passenger selector configured to receive the call information of the passengers from each drone taxi and select the candidate passenger depending on whether a passenger is present;
• and
• a route optimizer configured to, every time the drone state information of each of the plurality of drone taxies is changed and updated, re-generate the travel route information of each drone taxi from the drone state information of the plurality of drone taxies using the optimization model trained through the multi-agent reinforcement learning and transmit the travel route information to each drone taxi,
• wherein the drone state information includes current location information, onboard passenger information, candidate passenger information, and vacant seat information, and
• wherein the optimization model is trained using a two-stage attention mechanism trained through multi-agent reinforcement learning.