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Zero-shot coordination (ZSC) is a new challenge focusing on generalizinglearned coordination skills to unseen partners. Existing methods train the egoagent with partners from pre-trained or evolving populations. The agent's ZSCcapability is typically evaluated with a few evaluation partners, includinghuman and agent, and reported by mean returns. Current evaluation methods forZSC capability still need to improve in constructing diverse evaluationpartners and comprehensively measuring the ZSC capability. We aim to create areliable, comprehensive, and efficient evaluation method for ZSC capability. Weformally define the ideal 'diversity-complete' evaluation partners and proposethe best response (BR) diversity, which is the population diversity of the BRsto the partners, to approximate the ideal evaluation partners. We propose anevaluation workflow including 'diversity-complete' evaluation partnersconstruction and a multi-dimensional metric, the Best Response Proximity(BR-Prox) metric. BR-Prox quantifies the ZSC capability as the performancesimilarity to each evaluation partner's approximate best response,demonstrating generalization capability and improvement potential. Were-evaluate strong ZSC methods in the Overcooked environment using the proposedevaluation workflow. Surprisingly, the results in some of the most used layoutsfail to distinguish the performance of different ZSC methods. Moreover, theevaluated ZSC methods must produce more diverse and high-performing trainingpartners. Our proposed evaluation workflow calls for a change in how weefficiently evaluate ZSC methods as a supplement to human evaluation.

Quantifying Zero-shot Coordination Capability with Behavior Preferring Partners