New LQR algorithm bypasses stability requirement, improves sample complexity

Researchers have developed a new receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem, which addresses unknown system dynamics. This novel approach improves upon existing methods by not requiring two-point gradient estimates and maintaining the same sample complexity. A key advancement is the elimination of the need for an initially stable policy, making the algorithm more broadly applicable. The research also refines the analysis of error propagation through the Riccati operator using Riemannian distances, leading to enhanced sample complexity and convergence guarantees. AI

IMPACT Introduces a more broadly applicable and efficient algorithm for control systems, potentially impacting areas that use reinforcement learning for dynamic control.

RANK_REASON Academic paper detailing a new algorithm for a control theory problem. [lever_c_demoted from research: ic=1 ai=0.7]

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• Amirreza Neshaei Moghaddam
• arXiv
• linear-quadratic regulator
• reinforcement learning
• Riccati operator

• paper
• other

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