Multi-model Optimization with Discounted Reward and Budget Constraint

Multiple arm bandit algorithm is widely used in gaming, gambling, policy generation, and artificial intelligence projects and gets more attention recently. In this paper, we explore non-stationary reward MAB problem with limited query budget. An upper confidence bound (UCB) based algorithm for the discounted MAB budget finite problem, which uses reward-cost ratio instead of arm rewards in discount empirical average. In order to estimate the instantaneous expected reward-cost ratio, the DUCB-BF policy averages past rewards with a discount factor giving more weight to recent observations. Theoretical regret bound is established with proof to be over-performed than other MAB algorithms. A real application on maintenance recovery models refinement is explored. Results comparison on 4 different MAB algorithms and DUCB-BF algorithm yields lowest regret as expected.