Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games

arXiv:2402.18781v1 Announce Type: cross
Abstract: Stochastic games arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures, where information asymmetry presents challenges for decision-making entities (players). Existing computational methods for asymmetric information stochastic games (AISG) are primarily offline, targeting special classes of AISGs to avoid belief hierarchies, and lack online adaptability to deviations from equilibrium. To address this limitation, we propose a conjectural online learning (COL), a learning scheme for generic AISGs. COL, structured as a forecaster-actor-critic …

abstract arxiv belief challenges computational cs.gt cs.lg cs.sy cyber decision eess.sy games information making offline online learning stochastic systems targeting technical type