Regret Analysis of Policy Optimization over Submanifolds for Linearly Constrained Online LQG

מושגי ליבה

Proposing an online second-order method, OONM, based on a Riemannian metric for linearly constrained online LQG problems.

תקציר

Recent advancements in online optimization and control have led to the study of linear quadratic regulator (LQR) problems with varying cost matrices. The proposed OONM algorithm leverages predictions on first and second-order function information to provide adaptive control in real-time. Regret is defined as the sub-optimality of cumulative costs compared to a minimizing controller sequence. Existing works typically parameterize controllers linearly, lacking practical conditions like sparsity. The study extends the offline LQR setup to an online scenario with linear constraints, focusing on regret against minimizing policies. Simulation results confirm the effectiveness of OONM in providing adaptive control.

סטטיסטיקה

Recent advancement in online optimization and control.
Second-order method for linearly constrained LQR.
Regret defined as sub-optimality of cumulative costs.
Controller parameterization challenges due to lack of sparsity.
Extension of offline LQR setup to online with linear constraints.

ציטוטים

"The proposed OONM algorithm leverages predictions on first and second-order function information."
"Regret is defined as the sub-optimality of cumulative costs compared to a minimizing controller sequence."
"The study extends the offline LQR setup to an online scenario with linear constraints."
"Simulation results confirm the effectiveness of OONM in providing adaptive control."

תובנות מפתח מזוקקות מ:

Regret Analysis of Policy Optimization over Submanifolds for Linearly Constrained Online LQG

by Ting-Jui Cha... ב- arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08553.pdf

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