关键词: 多摇臂算法, 动态定价, 置信区间上界算法

Abstract:

Considering that dynamic pricing is a nonstationary Multi-Armed Bandit（MAB） problem. That is, the profit of the manufacturer is time-varying, so based on the previous research, this paper studies the application of the Upper Confidence Bound（UCB） algorithm with time-varying reward in dynamic pricing. This paper describes the pricing problem as a multi-armed problem, and constructs a profit maximization model to get the optimal solution. The simulation results show that the proposed algorithm converges faster, the rewards learned are closer to the real rewards. Compared with previous studies, this model takes time-varying factors into account, which is more in line with the dynamic pricing in the real scenario, and provides the corresponding decision support for the pricing of manufacturers.

Key words: multi-armed bandit algorithm, dynamic pricing, upper confidence bound