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Researchers achieve near-optimal regret in safe learning-based control for constrained LQR

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a new algorithm for adaptive control of stochastic linear quadratic regulators with constraints. This algorithm achieves near-optimal regret of $\tilde{O}(\sqrt{T})$ and satisfies chance constraints, which allows for handling unbounded noise. The method involves selecting an optimistic policy using semidefinite programming and then scaling it back to ensure safety, with theoretical guarantees derived from a novel covariance-based analysis. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a novel control algorithm with theoretical guarantees for constrained systems, potentially impacting robotics and autonomous systems.

RANK_REASON This is a research paper published on arXiv detailing a new algorithm for a specific control problem.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Spencer Hutchinson, Nanfei Jiang, Mahnoosh Alizadeh ·

    Near-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator

    arXiv:2604.22158v1 Announce Type: cross Abstract: We study the problem of adaptive control of the stochastic linear quadratic regulator (LQR) with constraints that must be satisfied at every time step. Prior work on the multidimensional problem has shown $\tilde{O}(T^{2/3})$ regr…

  2. arXiv cs.LG TIER_1 · Mahnoosh Alizadeh ·

    Near-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator

    We study the problem of adaptive control of the stochastic linear quadratic regulator (LQR) with constraints that must be satisfied at every time step. Prior work on the multidimensional problem has shown $\tilde{O}(T^{2/3})$ regret and satisfaction of robust constraints, leaving…

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