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A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prior knowledge of the transition model. Each team member makes decisions based on local private information and delayed common information shared across the team. Using only this available information, each member learns an approximate low-rank Markov decision process and applies le

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