Auto-Encoding Bayesian Inverse Games

arXiv:2402.08902v1 Announce Type: cross
Abstract: When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling. In interactive motion planning, however, agents typically do not have access to a complete model of the game, e.g., due to unknown objectives of other players. Therefore, we consider the inverse game problem, in which some properties of the game are unknown a priori and must be inferred from observations. Existing maximum …

abstract agent agents arxiv auto bayesian cs.gt cs.lg cs.ma cs.ro cs.sy decisions dynamic eess.sy encoding environment future game games impact interactive motion planning multiple planning type