Optimal Learning for Structured Bandits
针对存在凸结构信息的多臂老虎机问题，提出DUSA算法，其遗憾与信息论下界匹配，能处理线性、Lipschitz等结构化老虎机，是首个计算可行的渐近最优策略。
We study structured multiarmed bandits, which is the problem of online decision-making under uncertainty in the presence of structural information. In this problem, the decision-maker needs to discover the best course of action despite observing only uncertain rewards over time. The decision-maker is aware of certain convex structural information regarding the reward distributions; that is, the decision-maker knows that the reward distributions of the arms belong to a convex compact set. In the presence of such structural information, the decision-maker then would like to minimize his or her regret by exploiting this information, where the regret is its performance difference against a benchmark policy that knows the best action ahead of time. In the absence of structural information, the classical upper confidence bound (UCB) and Thomson sampling algorithms are well known to suffer minimal regret. However, as recently pointed out by Russo and Van Roy (2018) and Lattimore and Szepesvari (2017) , neither algorithm is capable of exploiting structural information that is commonly available in practice. We propose a novel learning algorithm that we call “DUSA,” whose regret matches the information-theoretic regret lower bound up to a constant factor and can handle a wide range of structural information. Our algorithm DUSA solves a dual counterpart of the regret lower bound at the empirical reward distribution and follows its suggested play. We show that this idea leads to the first computationally viable learning policy with asymptotic minimal regret for various structural information, including well-known structured bandits such as linear, Lipschitz, and convex bandits and novel structured bandits that have not been studied in the literature because of the lack of a unified and flexible framework. Funding: N. Golrezaei was supported in part by the Young Investigator Program (YIP) Award from the Office of Naval Research (ONR) [Grant N00014-21-1-2776] and the MIT Research Support Award. This paper was accepted by Chung Piaw Teo, Optimization