计算机应用与软件2024，Vol.41Issue(4)：228-235,261,9.DOI:10.3969/j.issn.1000-386x.2024.04.035
基于深度强化学习的高铁客票动态定价算法
DYNAMIC PRICING ALGORITHM FOR HIGH-SPEED RAIL TICKETS BASED ON DEEP REINFORCEMENT LEARNING
摘要
Abstract
This paper aims to solve the dynamic pricing problem of high-speed rail tickets under the unknown demand function.To maximize the expected return of a single train,we constructed a Markov multi-stage decision model and designed a DQN(Deep Q Net)reinforcement learning framework to find the optimal strategy for dynamic pricing.The algorithm used the day's income as the reward,and approximated the expected optimal return of all state-action combinations using a neural network.A high-speed rail passenger transport demand simulator was developed based on the market dynamics and passenger behavior for verifying the performance of the algorithm.The experimental results show that the agent dynamic pricing strategy can adjust the price flexibly under different demand levels,and its performance is close to the theoretical upper bound and better than the comparison strategy significantly.关键词
收益管理/高铁客票定价/动态定价/动态规划/强化学习/环境模拟算法Key words
Revenue management/High-speed rail tickets pricing/Dynamic pricing/Dynamic planning/Rein-forcement learning/Environment simulation algorithm分类
信息技术与安全科学引用本文复制引用
毕文杰,陈功..基于深度强化学习的高铁客票动态定价算法[J].计算机应用与软件,2024,41(4):228-235,261,9.基金项目
国家自然科学基金项目(71871231,91646115). （71871231,91646115）
