Linear Bandits under Exact Sliding-Window Constraints
This research studies linear bandits where actions must follow strict time-window constraints. It proves that for specific constraint types, a stationary solution is optimal or nearly optimal offline. For online learning, it introduces metrics like transition diameter to bound regret using rare policy updates. The work shows how history and feasibility can be modeled as a control problem.