Continuous Online Learning and New Insights to Online Imitation Learning
From MaRDI portal
Publication:6330321
arXiv1912.01261MaRDI QIDQ6330321
Author name not available (Why is that?)
Publication date: 3 December 2019
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 across rounds with respect to the learner's decisions. We show that COL covers and more appropriately describes many interesting applications, from general equilibrium problems (EPs) to optimization in episodic MDPs. Using this new setup, we revisit the difficulty of achieving sublinear dynamic regret. We prove that there is a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs, and we present a reduction from dynamic regret to both static regret and convergence rate of the associated EP. At the end, we specialize these new insights into online imitation learning and show improved understanding of its learning stability.
This page was built for publication: Continuous Online Learning and New Insights to Online Imitation Learning
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6330321)
