Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning
This research investigates reinforcement learning where available actions change every episode based on a context vector. The proposed Contextual MVP algorithm achieves near-optimal regret bounds, matching standard RL results under certain conditions. It also provides a new analysis showing how performance depends on the size of the gap between optimal and sub-optimal actions.