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arXiv AI By Maria Ana Cardei, Matthew Landers, Afsaneh Doryab

Coordination Matters: Process-Aware Evaluation of Cooperative Multi-Agent Reinforcement Learning

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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