Papers › Weighted Linear Bandits for Non-Stationary Environments

Weighted Linear Bandits for Non-Stationary Environments

19 Sep 2019NeurIPS 2019 12arXiv:1909.09146archive 2025-07-28

Yoan Russac, Claire Vernade, Olivier Cappé

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose D-LinUCB, a novel optimistic algorithm based on discounted linear regression, where exponential weights are used to smoothly forget the past. This involves studying the deviations of the sequential weighted least-squares estimator under generic assumptions. As a by-product, we obtain novel deviation results that can be used beyond non-stationary environments. We provide theoretical guarantees on the behavior of D-LinUCB in both slowly-varying and abruptly-changing environments. We obtain an upper bound on the dynamic regret that is of order d^{2/3} B\_T^{1/3}T^{2/3}, where B\_T is a measure of non-stationarity (d and T being, respectively, dimension and horizon). This rate is known to be optimal. We also illustrate the empirical performance of D-LinUCB and compare it with recently proposed alternatives in simulated environments.

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detection_sorted YRussac/WeightedLinearBandits/utils.py community (archive-listed) unverified BSD-3-Clause (permissive) · 7c59eacc47ff329f · report
generate_smooth_theta YRussac/WeightedLinearBandits/utils.py community (archive-listed) unverified BSD-3-Clause (permissive) · ba42a0a5aabd6170 · report
get_B_T_smooth YRussac/WeightedLinearBandits/utils.py community (archive-listed) unverified BSD-3-Clause (permissive) · e0a687ac7c776485 · report

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Linear Regression

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