DYNAMIC PRICING ALGORITHM FOR HIGH-SPEED RAIL TICKETS BASED ON DEEP REINFORCEMENT LEARNING
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.
国家科技期刊平台
NSTL
万方数据
维普
