2023/11/02 by Bryan Chan, Chan, Bryan, Karime Pereida +3
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.2311.01589
openalex publication_date 2023/11/02 · openalex created_date 2023/11/07 · openalex updated_date 2026/07/28
Transferring representation for multitask imitation learning has the potential to provide improved sample efficiency on learning new tasks, when compared to learning from scratch. In this work, we provide a statistical guarantee indicating that we can indeed achieve improved sample efficiency on the target task when a representation is trained using sufficiently diverse source tasks. Our theoretical results can be readily extended to account for commonly used neural network architectures with realistic assumptions. We conduct empirical analyses that align with our theoretical findings on four simulated environments\unicodex2014in particular leveraging more data from source tasks can improve sample efficiency on learning in the new task.