# A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing

arXiv: https://arxiv.org/abs/2609.26783 · HTML: https://mlsanity.com/paper/2609.26783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 26 of 26)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs](https://mlsanity.com/paper/2006.10814/) (2020)
- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [Representation Learning for Online and Offline RL in Low-rank MDPs](https://mlsanity.com/paper/2110.04652/) (2021)
- [Model-free Representation Learning and Exploration in Low-rank MDPs](https://mlsanity.com/paper/2102.07035/) (2021)
- [Approximate information state for approximate planning and reinforcement learning in partially observed systems](https://mlsanity.com/paper/2010.08843/) (2020)
- [Sample-Efficient Reinforcement Learning of Undercomplete POMDPs](https://mlsanity.com/paper/2006.12484/) (2020)
- [An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.03052/) (2024)
- [Provable Reinforcement Learning with a Short-Term Memory](https://mlsanity.com/paper/2202.03983/) (2022)
- [Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems](https://mlsanity.com/paper/2206.12020/) (2022)
- [Representation Learning for General-sum Low-rank Markov Games](https://mlsanity.com/paper/2210.16976/) (2022)
- [Provable Partially Observable Reinforcement Learning with Privileged Information](https://mlsanity.com/paper/2412.00985/) (2024)
- [POMDP-lite for Robust Robot Planning under Uncertainty](https://mlsanity.com/paper/1602.04875/) (2016)
- [On Team Decision Problems with Nonclassical Information Structures](https://mlsanity.com/paper/2101.10992/) (2021)
- [Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning](https://mlsanity.com/paper/2311.12244/) (2023)
- [Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.12603/) (2021)
- [Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.02179/) (2022)
- [Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP](https://mlsanity.com/paper/2306.12356/) (2023)
- [A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways](https://mlsanity.com/paper/2109.11672/) (2021)
- [AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning](https://mlsanity.com/paper/2504.20187/) (2025)
- [A Dynamic Program for a Team of Two Agents with Nested Information](https://mlsanity.com/paper/2103.10028/) (2021)
- [Deviation Between Team-Optimal Solution and Nash Equilibrium in Flow Assignment Problems](https://mlsanity.com/paper/2503.23991/) (2025)
- [When Does Selfishness Align with Team Goals? A Structural Analysis of Equilibrium and Optimality](https://mlsanity.com/paper/2508.13450/) (2025)

## 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.