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Imitation learning uses data for training policies to solve complex tasks.However, when the training data is collected from human demonstrators, it oftenleads to multimodal distributions because of the variability in human actions.Most imitation learning methods rely on a maximum likelihood (ML) objective tolearn a parameterized policy, but this can result in suboptimal or unsafebehavior due to the mode-averaging property of the ML objective. In this work,we propose Information Maximizing Curriculum, a curriculum-based approach thatassigns a weight to each data point and encourages the model to specialize inthe data it can represent, effectively mitigating the mode-averaging problem byallowing the model to ignore data from modes it cannot represent. To cover allmodes and thus, enable diverse behavior, we extend our approach to a mixture ofexperts (MoE) policy, where each mixture component selects its own subset ofthe training data for learning. A novel, maximum entropy-based objective isproposed to achieve full coverage of the dataset, thereby enabling the policyto encompass all modes within the data distribution. We demonstrate theeffectiveness of our approach on complex simulated control tasks using diversehuman demonstrations, achieving superior performance compared tostate-of-the-art methods.

Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills