{
  "$type": "site.standard.document",
  "description": "A method for an online, task-aware opponent modeling in autonomous racing is described. The method includes concurrently training an opponent-aware policy and an opponent-aware encoder using reinforcement learning. The method also includes calculating, by the opponent-aware encoder, opponent…",
  "path": "/patents/1377319",
  "publishedAt": "2025-12-11T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06N3/092",
    "TOYOTA RESEARCH INSTITUTE, INC."
  ],
  "textContent": "A method for an online, task-aware opponent modeling in autonomous racing is described. The method includes concurrently training an opponent-aware policy and an opponent-aware encoder using reinforcement learning. The method also includes calculating, by the opponent-aware encoder, opponent encoding information according to prior opponent positions. The method further includes updating learning parameters of the opponent-aware policy using the opponent encoding information from the opponent-aware encoder to predict actions. The method also includes updating a posterior network according to an auxiliary mutual information loss between the actions predicted by the opponent-aware policy and the opponent encoding information from the opponent-aware encoder.",
  "title": "SYSTEM AND METHOD FOR ONLINE, TASK-AWARE OPPONENT MODELING IN AUTONOMOUS RACING"
}