A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
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 least-squares value iteration to compute its policy. This yields a fully decentralized learning and...
| Authors | Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos |
|---|---|
| Primary category | math.OC |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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