Self-Generating Data for Goal-Conditioned Compositional ProblemsDownload PDF

Published: 03 Mar 2023, Last Modified: 12 Apr 2023RRL 2023 PosterReaders: Everyone
Abstract: Building reinforcement learning agents that are generalizable to compositional problems has long been a research challenge. Recent success relies on a pre-existing dataset of rich behaviors. We present a novel paradigm to learn policies generalizable to compositional tasks with self-generated data. After learning primitive skills, the agent runs task expansion that actively expands out more complex tasks by composing learned policies and also naturally generates a dataset of demonstrations for self-distillation. In a proof-of-concept block-stacking environment, our agent discovers a large number of complex tasks after multiple rounds of data generation and distillation, and achieves an appealing zero-shot generalization success rate when building human-designed shapes.
Track: Technical Paper
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