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Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems

NeurIPS 2022 · Bandits and online learning › Contextual bandits and Thompson sampling · approach: Gaussian process bandit regret · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use augmented RBMLE (ball-constrained) instead of unaugmented RBMLE (ablation)RBMLE · four LQ systemsregret at T=500: 3233 -> 3233; 5930 -> 5930; 16144 -> 16135; 540297 -> 528805No clear effect
use ARBMLE instead of OFULQadaptive LQ control · Unstable Laplacianregret at T=500: 1.2 \times 10^6 -> 3233Helped
use ARBMLE instead of Thompson Samplingadaptive LQ control · UAVregret at T=500: 1.1 \times 10^20 -> 16135Helped
use ARBMLE instead of StabLadaptive LQ control · Boeing 747regret at T=500: 1.4 \times 10^{7} -> 528805Helped
use ARBMLE instead of RCEadaptive LQ control · UAVregret at T=500: 180639 -> 16135Helped
use ARBMLE instead of Input Perturbations (IP)adaptive LQ control · Boeing 747regret at T=500: 540248 -> 528805Helped
use RBMLE instead of IP on chained integratoradaptive LQ control · Chained integratorregret at T=500: 2337 -> 2322Helped
use RBMLE instead of IP on stabilizable-not-controllable systemadaptive LQ control · Stabilizable not controllableregret at T=500: 15628 -> 15665Hurt
use ARBMLE instead of OFULQ on chained integratoradaptive LQ control · Chained integratorregret at T=500: 33449 -> 2322Helped

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