MA-BC enables efficient multi-objective imitation

A paper introduces MA-BC for learning from experts with different objectives: it pools demonstrations only where observed actions don't disagree, balancing trade-offs with data sharing, and provides upper and lower bounds on sample complexity. Authors are Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher, and Claire Vernade.