We extend knowledge gradient (KG) policy for the multi-objective multi-armed bandit problems to efficiently#R##N##R##N#explore the Pareto optimal arms.#R##N##R##N#We consider two partial order relationships to order the mean vectors, i.e. #R##N##R##N#Pareto and scalarized functions.#R##N##R##N#Pareto KG finds the optimal arms using Pareto search, while the scalarizations-KG transform the #R##N##R##N#multi-objectives arms into one-objective arm to find the optimal arms. #R##N##R##N#To measure the performance of the proposed algorithms, we propose three regret measures.#R##N##R##N#We compare the performance of knowledge gradient policy with UCB1 on a multi-objective #R##N##R##N#multi-armed bandit problem, where KG outperforms UCB1.
['Saba Q. Yahyaa', 'Mm Madalina Drugan', 'Bernard Manderick']
Knowledge Gradient for Multi-objective Multi-armed Bandit Algorithms