Restless Linear Bandits

• URL: http://arxiv.org/abs/2405.10817v1
• Date: Fri, 17 May 2024 14:37:39 GMT
• Title: Restless Linear Bandits
• Authors: Azadeh Khaleghi,
• Abstract summary: It is assumed that there exists an unknown $mathbbRd$-valued stationary $varphi$-mixing sequence of parameters $(theta_t,t in mathbbN)$ which gives rise to pay-offs. An optimistic algorithm, called LinMix-UCB, is proposed for the case where $theta_t$ has an exponential mixing rate.
• Score: 5.00389879175348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A more general formulation of the linear bandit problem is considered to allow for dependencies over time. Specifically, it is assumed that there exists an unknown $\mathbb{R}^d$-valued stationary $\varphi$-mixing sequence of parameters $(\theta_t,~t \in \mathbb{N})$ which gives rise to pay-offs. This instance of the problem can be viewed as a generalization of both the classical linear bandits with iid noise, and the finite-armed restless bandits. In light of the well-known computational hardness of optimal policies for restless bandits, an approximation is proposed whose error is shown to be controlled by the $\varphi$-dependence between consecutive $\theta_t$. An optimistic algorithm, called LinMix-UCB, is proposed for the case where $\theta_t$ has an exponential mixing rate. The proposed algorithm is shown to incur a sub-linear regret of $\mathcal{O}\left(\sqrt{d n\mathrm{polylog}(n) }\right)$ with respect to an oracle that always plays a multiple of $\mathbb{E}\theta_t$. The main challenge in this setting is to ensure that the exploration-exploitation strategy is robust against long-range dependencies. The proposed method relies on Berbee's coupling lemma to carefully select near-independent samples and construct confidence ellipsoids around empirical estimates of $\mathbb{E}\theta_t$.

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