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Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems

ICML 2018 · Bandits and online learning › Contextual bandits and Thompson sampling · approach: Thompson sampling analysis · 52 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Frequent-update TS (new parameter sampled each step) instead of lazy episode-based updatesThompson sampling for LQ control · Linear quadratic control, n=1cumulative regret order: O(T^{2/3}) to O(sqrt(T))Helped
Novel bound on regret from policy switches via expected absolute deviation of Riccati solution under TS distributionThompson sampling for LQ control · Linear quadratic control, any dimensioncumulative gap regret RgapHelped
Extension of the optimal-probability lower bound from n=1 to multi-dimensional systemsThompson sampling for LQ control · Multi-dimensional LQ systems (numerical)probability of being optimistic p_optNo clear effect

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