Invariant description for batch version of UCB strategy for multi-armed bandit

Abstract

Abstract We consider a variation of upper confidence bound strategy for multi-armed bandit in batch processing setting. Invariant descriptions with the unit control horizon are obtained for upper bounds in the strategy and for regret. A set of Monte-Carlo simulations are performed for different settings of MABs to determine the minimax regret for multi-armed bandits with different configurations.