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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.
Stop hand-tuning reward learning losses: MAVRL learns a shared reward function from diverse feedback signals by treating each as a likelihood within a Bayesian inference framework.
