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We consider the task of building strong but human-like policies inmulti-agent decision-making problems, given examples of human behavior.Imitation learning is effective at predicting human actions but may not matchthe strength of expert humans, while self-play learning and search techniques(e.g. AlphaZero) lead to strong performance but may produce policies that aredifficult for humans to understand and coordinate with. We show in chess and Gothat regularizing search based on the KL divergence from an imitation-learnedpolicy results in higher human prediction accuracy and stronger performancethan imitation learning alone. We then introduce a novel regret minimizationalgorithm that is regularized based on the KL divergence from animitation-learned policy, and show that using this algorithm for search inno-press Diplomacy yields a policy that matches the human prediction accuracyof imitation learning while being substantially stronger.

Modeling Strong and Human-Like Gameplay with KL-Regularized Search