Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) benchmarks commonly emphasize aggregate outcomes such as return, success rate, or completion time. While essential, these metrics often fail to reveal how agents coordinate, particularly in settings where agents, tasks, and joint assignment choices scale combinatorially. We propose a coordination-aware evaluation perspective that supplements return with process-level diagnostics. We instantiate this perspective using STAT, a controlled commitment-constrained spatial task-allocation testbed that systematically varies agents, tasks, and environment size while holding observation access and task rules fixed. We evaluate six representative...
| Authors | Maria Ana Cardei, Matthew Landers, Afsaneh Doryab |
|---|---|
| Primary category | cs.MA |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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