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Active policy selection, not just expert samples, is essential for strategically identifying rewards in multi-agent games, enabling near-optimal downstream decision-making.
Forget state-action spaces: this work achieves efficient multi-agent imitation learning by concentrating on feature-level representations in linear Markov games.