Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning

Researchers from the academic community have introduced a new evaluation framework for cooperative multi-agent reinforcement learning (MARL), addressing limitations in current benchmarks that primarily focus on aggregate outcomes like return or success rates. The study argues that these traditional metrics often fail to reveal the underlying coordination mechanisms among agents, especially in complex, combinatorial settings. To address this, the authors propose a coordination-aware evaluation perspective supplemented by process-level diagnostics. They instantiated this approach using STAT, a controlled testbed for spatial task allocation that systematically varies agents, tasks, and environment size. By evaluating six representative value-based MARL methods, the results demonstrated that similar return trends could mask distinct coordination behaviors, such as redundant assignments and varying efficiency levels. The findings highlight that performance under scale is influenced by assignment pressure and sparse decision opportunities, not just action-space size. This research advocates for coordination-aware evaluation as a necessary complement to standard return-based benchmarking, aiming to improve the development and understanding of robust cooperative AI systems in dynamic environments.