Learned Coordination Conventions in Cooperative MARL: Measuring the Translation Gap Between Theory-Informed Roles and Learned Routing
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. We study this translation gap between theory-informed role expectations and learned coordination structure through a diagnostic combining a role-routing matrix, formation sensitivity ($\Delta_{\max}$), and gradient/occlusion attribution across three-role MiniGrid and SMACv2 (Terran) envir
The proliferation of advanced AI systems necessitates deeper understanding of emergent coordination, which is a critical challenge as MARL systems become more complex and autonomous.
Understanding how autonomous agents form coordination conventions is crucial for designing reliable, transparent, and controllable AI systems, especially in high-stakes environments.
This research provides a framework for analyzing the discrepancy between human-designed roles and agent-learned behaviors, offering a diagnostic tool for assessing emergent AI coordination.
- · AI researchers
- · Developers of multi-agent systems
- · Ethical AI organizations
- · Developers neglecting emergent coordination issues
- · Systems highly reliant on pre-programmed behavioral rules without adaptation
Improved methodologies for evaluating and validating multi-agent AI system behavior will emerge.
This could lead to more robust and explainable autonomous AI agents capable of complex tasks.
The enhanced understanding of emergent AI coordination could inform the development of more effective human-AI collaborative frameworks.
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Read at arXiv cs.AI