Decentralized Team Learning for POMDPs With Delayed Sharing Closely Matches Centralized Policies
A team-based approach learns a low-rank latent model from delayed common information and runs local value iteration, achieving near-centralized performance without a centralized coordinator.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
The study shows that each team member, using only private observations and delayed common information, can learn a low-rank representation of the environment and compute policies that closely approximate a centralized team solution, with finite-sample guarantees.
Why it matters
This suggests organizations can coordinate multi-agent work with limited, delayed data while still aligning with a central goal, reducing reliance on heavy centralized control.
Practically, the approach relies on a shared latent dynamics representation learned from delayed, team-wide information. Each member updates beliefs locally and uses value iteration to decide actions, balancing exploration with a known model. The method preserves team goals while avoiding centralized training or control.
In real-world terms, this could help distributed teams,like field technicians or autonomous-vehicle fleets,coordinate more effectively when shared data is noisy, delayed, or costly to centralize, though performance depends on data coverage and memory assumptions.
Original sources · 1
- A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing ↗arXiv · 2026-09-22
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
The results depend on finite-memory approximations, sample coverage, and delayed information assumptions; real-world data may require more exploration and robust modeling.
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