Multinomial Logit Bandit with Low Switching Cost

We study multinomial logit bandit with limited adaptivity, where the algorithms change their exploration actions as infrequently as possible when achieving almost optimal minimax regret. We propose two measures of adaptivity: the assortment switching cost and the more fine-grained item switching cost. We present an anytime algorithm (AT-DUCB) with O(N log T) assortment switches, almost matching the lower bound Omega(fracN log T log log T). In the fixed-horizon setting, our algorithm FH-DUCB incurs O(N log log T) assortment switches, matching the asymptotic lower bound. We also present the ESUCB algorithm with item switching cost O(N log2 T).