{
  "$type": "site.standard.document",
  "description": "Provided are a drone taxi system based on multi-agent reinforcement learning and a drone taxi operation method using the same. The drone taxi system includes a plurality of drone taxies configured to receive call information including departure point information and destination information from…",
  "path": "/patents/1327608",
  "publishedAt": "2022-09-22T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06Q10/0631",
    "Korea University Research and Business Foundation"
  ],
  "textContent": "Provided are a drone taxi system based on multi-agent reinforcement learning and a drone taxi operation method using the same. The drone taxi system includes a plurality of drone taxies configured to receive call information including departure point information and destination information from passenger terminals present within a certain range and a control server configured to receive call information of passengers from each drone taxi, select a candidate passenger depending on whether a passenger is present, generate travel route information of each drone taxi from drone state information of the plurality of drone taxies through multi-agent reinforcement learning, and transmit the travel route information to the drone taxi.",
  "title": "DRONE TAXI SYSTEM BASED ON MULTI-AGENT REINFORCEMENT LEARNING AND DRONE TAXI OPERATION USING THE SAME"
}