arXiv:2606. 29541v1 Announce Type: new Abstract: Role-semantic assignments provide priors over how heterogeneous agents may coordinate, but cooperative MARL systems instead settle on conventions through decentralized, non-stationary learning, with no guarantee that the resulting structure matches those priors.
By Yoosung Hong
arXiv:2606. 25073v1 Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or task.
By Animesh Animesh, Satheesh K Perepu, Kaushik Dey
arXiv:2609.39727v1 Announce Type: new
Abstract: Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, ye...
By Oana Madalina Fron, Ojas Shirekar, Chirag Raman
arXiv:2511. 02687v2 Announce Type: replace Abstract: The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with different information, privileges, and tools.
By Tim R. Davidson, Adam Fourney, Saleema Amershi, Robert West, Eric Horvitz, Ece Kamar
The paper introduces a process‑aware evaluation framework for cooperative multi‑agent reinforcement learning that supplements traditional outcome metrics with diagnostics of interference, coordinated task completion, participation, and coordination efficiency. Using the STAT testbed and two established benchmarks, the authors demonstrate that policies with similar returns can differ markedly in coordination behavior, and that reward shaping can alter coordination without affecting overall task return. The framework reveals distinct process signatures across spatial, task, and agent scaling and remains informative across different coordination mechanisms.
By Maria Ana Cardei, Matthew Landers, Afsaneh Doryab
SIGMA is a hierarchical framework for cooperative multi‑agent reinforcement learning that addresses structured noise effects—local correlations in noise-induced decision impacts among agents with strong task dependencies. It groups agents into adaptive local structures using density‑based clustering, aggregates intra‑group representations to smooth deviations, and then applies inter‑group attention to integrate information while respecting heterogeneous contributions. Experiments on noisy‑observation StarCraft II tasks confirm that SIGMA improves robustness to observation noise without sacrificing performance in clean environments.
By Li Mingqian