Periodic Bandits and Wireless Network Selection

Abstract

Bandit-style algorithms have been studied extensively in stochastic and adversarial settings. Such algorithms have been shown to be useful in multiplayer settings, e.g. to solve the wireless network selection problem, which can be formulated as an adversarial bandit problem. A leading bandit algorithm for the adversarial setting is EXP3. However, network behavior is often repetitive, where user density and network behavior follow regular patterns. Bandit algorithms, like EXP3, fail to provide good guarantees for periodic behaviors. A major reason is that these algorithms compete against fixed-action policies, which is ineffective in a periodic setting.In this paper, we define a periodic bandit setting, and periodic regret as a better performance measure for this type of setting. Instead of comparing an algorithm's performance to fixed-action policies, we aim to be competitive with policies that play arms under some set of possible periodic patterns F (for example, all possible periodic functions with periods 1, 2, • • • , P ). We propose Periodic EXP4, a computationally efficient variant of the EXP4 algorithm for periodic settings. With K arms, T time steps, and where each periodic pattern in F is of length at most P , we show that the periodic regret obtained by Periodic EXP4 is at most O P KT log K + KT log |F | . We also prove a lower bound of Ωfor the periodic setting, showing that this is optimal within log-factors. As an example, we focus on the wireless network selection problem. Through simulation, we show that Periodic EXP4 learns the periodic pattern over time, adapts to changes in a dynamic environment, and far outperforms EXP3.

Dynamic Structure Estimation from Bandit Feedback

“…Auer et al [2019], Besbes et al [2014], , Cheung et al [2022], Luo et al [2018], Russac et al [2019], Trovo et al [2020], Wu et al [2018]) although they do not deal with periodically behaved dynamical system properly (see discussions in [Cai et al, 2021] as well). For discrete action settings, periodic bandit [Oh et al, 2019] was proposed, which aims at optimizing for the total regret. Also, if the period is known, Gaussian process bandit for periodic reward functions was proposed (Cai et al [2021]) under Gaussian noise assumption.…”