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On Diffusion Models for Multi-Agent Partial Observability: Shared Attractors, Error Bounds, and Composite Flow
Machine Learning · Submitted 17 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim With this bound, we propose a \emph{composite diffusion process} iterating over agents with theoretical convergence guarantees to the true state.
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Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approaches to addressing PO have appealed to deep learning models, providing a rigorous understanding of how these models and their approximation errors affect agents' handling of PO and their interactions remain a challenge. In addressing this challenge, we investigate reconstructing global states from local action-observation histories in Dec-POMDPs using diffusion models. We first find that diffusion models conditioned on local history represent possible states as stable fixed points. In collectively observable (CO) Dec-POMDPs, individual diffusion models conditioned on agents' local histories share a unique fixed point corresponding to the global state, while in non-CO settings, shared fixed points yield a distribution of possible states given joint history. We further find that, with deep learning approximation errors, fixed points can deviate from true states and the deviation is negatively correlated to the Jacobian rank. Inspired by this low-rank property, we bound a deviation by constructing a surrogate linear regression model that approximates the local behavior of a diffusion model. With this bound, we propose a \emph{composite diffusion process} iterating over agents with theoretical convergence guarantees to the true state.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Whereas recent approaches to addressing PO have appealed to deep learning models, providing a rigorous understanding of how these models and their approximation errors affect agents' handling of PO and their interactions remain a challenge. | gap 0.99 | 0.04 | 0.02 | asserted 0.96 |
| s2 | In addressing this challenge, we investigate reconstructing global states from local action-observation histories in Dec-POMDPs using diffusion models. | contribution 0.92 | 0.69 | 0.05 | asserted 0.74 |
| s3 | We first find that diffusion models conditioned on local history represent possible states as stable fixed points. | result 0.99 | 0.95 | 0.14 | asserted 1.00 |
| s4 | In collectively observable (CO) Dec-POMDPs, individual diffusion models conditioned on agents' local histories share a unique fixed point corresponding to the global state, while in non-CO settings, shared fixed points yield a distribution of possible states given joint history. | result 1.00 | 0.93 | 0.21 | asserted 1.00 |
| s5 | We further find that, with deep learning approximation errors, fixed points can deviate from true states and the deviation is negatively correlated to the Jacobian rank. | result 1.00 | 0.94 | 0.09 | asserted 0.95 |
| s6 | Inspired by this low-rank property, we bound a deviation by constructing a surrogate linear regression model that approximates the local behavior of a diffusion model. | method 0.86 | 0.92 | 0.08 | asserted 1.00 |
| s7 | With this bound, we propose a \emph{composite diffusion process} iterating over agents with theoretical convergence guarantees to the true state. | contribution 0.66 | 0.94 | 0.49 | asserted 1.00 |
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Original abstract
Multiagent systems grapple with partial observability (PO), and the decentralized POMDP (Dec-POMDP) model highlights the fundamental nature of this challenge. Whereas recent approaches to addressing PO have appealed to deep learning models, providing a rigorous understanding of how these models and their approximation errors affect agents' handling of PO and their interactions remain a challenge. In addressing this challenge, we investigate reconstructing global states from local action-observation histories in Dec-POMDPs using diffusion models. We first find that diffusion models conditioned on local history represent possible states as stable fixed points. In collectively observable (CO) Dec-POMDPs, individual diffusion models conditioned on agents' local histories share a unique fixed point corresponding to the global state, while in non-CO settings, shared fixed points yield a distribution of possible states given joint history. We further find that, with deep learning approximation errors, fixed points can deviate from true states and the deviation is negatively correlated to the Jacobian rank. Inspired by this low-rank property, we bound a deviation by constructing a surrogate linear regression model that approximates the local behavior of a diffusion model. With this bound, we propose a \emph{composite diffusion process} iterating over agents with theoretical convergence guarantees to the true state.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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Diffusion models have emerged as a powerful class of generative AI methods, spurring notable advances in a wide range of tasks such as image generation [ref] , video generation [ref] , molecular design [ref] , text generation [ref] , and multi-agent learning [ref] .
BundleFlow: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization · Preliminaries · 1.00
What it cites · 12 sentences without a confident label
Cites as background 8 papers
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Diffusion models [ref] have been extensively explored in single-agent settings, significantly advancing areas such as planning that require approximators of MDP dynamics [ref] , data synthesis for reinforcement learning [ref] , and policy training on offline datasets [ref] .
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies · Introduction · 0.96
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Diffusion models [ref] have been extensively explored in single-agent settings, significantly advancing areas such as planning that require approximators of MDP dynamics [ref] , data synthesis for reinforcement learning [ref] , and policy training on offline datasets [ref] .
Planning with Diffusion for Flexible Behavior Synthesis · Introduction · 0.96
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Diffusion models [ref] have been extensively explored in single-agent settings, significantly advancing areas such as planning that require approximators of MDP dynamics [ref] , data synthesis for reinforcement learning [ref] , and policy training on offline datasets [ref] .
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners · Introduction · 0.96
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