cs.MA · 2026-07-21 · No. 60

Multiagent Systems, 2026-07-21.

3 new papers in cs.MA. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Aggregate in the Advantage, Not the Ratio: A Canonical-Form Analysis of Cooperative Multi-Agent Policy Optimization

    Zijian Zhao, Sen Li

    cs.MA · cs.LG

    Multi-agent policy optimization, exemplified by PPO-based methods, is a key branch of cooperative Multi-Agent Reinforcement Learning (MARL). A central design question is how many neighboring agents\footnote{In this paper, "neighbors" refer not only to physical proximity but also to agents whose actions influence one another.} to aggregate in order to effectively utilize global information for cooperation. This decision must be made along two...

    arxiv.org/abs/2607.17924 · PDF

  2. 02

    PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks

    Wen Qiu, Zhiqiang He, Wei Zhao, Hiroshi Masui

    cs.MA · cs.LG · cs.NI

    Most reinforcement learning controllers for these networks assume stationary conditions, and the few that handle change react to the external environment while leaving the network's internal state unexamined. We show that sustained non-stationarity damages this internal state directly: as objectives shift, neurons progressively fall dormant and the shared policy loses the capacity to learn. The obvious remedy, resetting dormant neurons, is...

    arxiv.org/abs/2607.17922 · PDF

  3. 03

    Value-Aware Prediction for Robust Multi-Agent Coordination Under Communication Loss

    Kemal Devrim Kafadar, Eren Özaltun, Mahmud Efnan Şanlı, Feyza Orak, Emirhan Gazi, Kubilay Kağan Kömürcü, Nazım Kemal Üre

    cs.MA · cs.LG · cs.RO

    Robust multi-agent coordination relies heavily on inter-agent communication, which is frequently disrupted by physical and environmental constraints in real-world deployments. To maintain operation during these intermittent communication failures, agents can employ internal prediction models to estimate missing shared state information. However, predictors trained with standard reconstruction objectives treat all transitions equally. In a...

    arxiv.org/abs/2607.17914 · PDF

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