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

By PulseAugur Editorial · [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

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 →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

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

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · 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 English(EN) · 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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