Continuous Online Learning and New Insights to Online Imitation Learning

Cited by: 2|Bibtex|Views28
Other Links: arxiv.org

Abstract:

Online learning is a powerful tool for analyzing iterative algorithms. However, the classic adversarial setup sometimes fails to capture certain regularity in online problems in practice. Motivated by this, we establish a new setup, called Continuous Online Learning (COL), where the gradient of online loss function changes continuously ...More

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