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Robustly cooperating with unseen agents and human partners presentssignificant challenges due to the diverse cooperative conventions thesepartners may adopt. Existing Ad Hoc Teamwork (AHT) methods address thischallenge by training an agent with a population of diverse teammate policiesobtained through maximizing specific diversity metrics. However, priorheuristic-based diversity metrics do not always maximize the agent's robustnessin all cooperative problems. In this work, we first propose that maximizing anAHT agent's robustness requires it to emulate policies in the minimum coverageset (MCS), the set of best-response policies to any partner policies in theenvironment. We then introduce the L-BRDiv algorithm that generates a set ofteammate policies that, when used for AHT training, encourage agents to emulatepolicies from the MCS. L-BRDiv works by solving a constrained optimizationproblem to jointly train teammate policies for AHT training and approximatingAHT agent policies that are members of the MCS. We empirically demonstrate thatL-BRDiv produces more robust AHT agents than state-of-the-art methods in abroader range of two-player cooperative problems without the need for extensivehyperparameter tuning for its objectives. Our study shows that L-BRDivoutperforms the baseline methods by prioritizing discovering distinct membersof the MCS instead of repeatedly finding redundant policies.

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents