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A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing

arXiv 2609.26783 ↗First posted 22 Sep 2026math.OC cs.LG cs.SYcited by 0

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Abstract (arXiv)

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 planning algorithm that requires neither a centralized coordinator nor centralized training. We show that the resulting member-side solutions approximate the centralized team solution: despite partial observability, unknown dynamics, and delayed common information, each member recovers the corresponding component of an approximate team-optimal policy. We further establish finite-sample performance guarantees and derive a corresponding sample-complexity bound for the proposed algorithm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 22 Sep 2026.

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References · 26

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14 more
  1. 2022Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systemscited by 35
  2. 2022Representation Learning for General-sum Low-rank Markov Gamescited by 14
  3. 2024Provable Partially Observable Reinforcement Learning with Privileged Informationcited by 13
  4. 2016POMDP-lite for Robust Robot Planning under Uncertaintycited by 12
  5. 2021On Team Decision Problems with Nonclassical Information Structurescited by 10
  6. 2023Provable Representation with Efficient Planning for Partial Observable Reinforcement Learningcited by 10
  7. 2021Common Information based Approximate State Representations in Multi-Agent Reinforcement Learningcited by 8
  8. 2022Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learningcited by 8
  9. 2023Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDPcited by 8
  10. 2021A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadwayscited by 6
  11. 2025AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learningcited by 3
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  13. 2025Deviation Between Team-Optimal Solution and Nash Equilibrium in Flow Assignment Problemscited by 1
  14. 2025When Does Selfishness Align with Team Goals? A Structural Analysis of Equilibrium and Optimalitycited by 1

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