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arXiv · 2602.17315

Flickering Multi-Armed Bandits

Sourav Chakraborty·Amit Kiran Rege·Claire Monteleoni·Lijun Chen
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Abstract

We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, where accessibility of the next action is restricted to a subset dependent on the agent's current choice. We formalize these constraints through stochastically evolving graphs where actions are limited to local neighborhoods. This mobility-constrained structure imposes a dual challenge: the statistical requirement of information acquisition and the physical overhead of navigation. We analyze FMAB under i.i.d. Erd\H{o}s--R'enyi and Edge-Markovian process, proposing a two-phase lazy random walk algorithm for robust exploration. We establish high-probability sublinear regret bounds and prove near-optimality via a matching information-theoretic lower bound. Our results characterize the intrinsic cost of learning under local-move constraints, complemented by a robotic disaster-response simulation.

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Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen. 2026-02-19. Flickering Multi-Armed Bandits. https://arxiv.org/abs/2602.17315

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