Sample Complexity of Linear Quadratic Regulator Without Initial Stability

• URL: http://arxiv.org/abs/2502.14210v3
• Date: Sat, 04 Oct 2025 17:34:14 GMT
• Title: Sample Complexity of Linear Quadratic Regulator Without Initial Stability
• Authors: Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard,
• Abstract summary: Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics.<n>Unlike prior methods, our algorithm avoids reliance on two-point gradient estimates while maintaining the same order of sample complexity.
• Score: 7.245261469258501
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics. Unlike prior methods, our algorithm avoids reliance on two-point gradient estimates while maintaining the same order of sample complexity. Furthermore, it eliminates the restrictive requirement of starting with a stable initial policy, broadening its applicability. Beyond these improvements, we introduce a refined analysis of error propagation through the contraction of the Riccati operator under the Riemannian distance. This refinement leads to a better sample complexity and ensures improved convergence guarantees.

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