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Forget state-action spaces: this work achieves efficient multi-agent imitation learning by concentrating on feature-level representations in linear Markov games.
Even perfect imitation of expert behavior in multi-agent settings can lead to highly exploitable policies, unless experts play dominant strategies or exhibit best-response continuity.
Stop wrestling with complex setups: this new benchmark makes evaluating machine unlearning algorithms as easy as running a script.
