A Study on Dynamic Pricing in the Airline Industry Using Reinforcement Learning Analyzing the Impact of Reinforcement Learning on Airline Pricing Strategies

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Dynamic pricing serves as an essential tactic in the airline sector, allowing airlines to modify ticket rates in response to changing market demand, rivalry, and various other influencing elements. This research investigates the use of Reinforcement Learning (RL) in dynamic pricing strategies, emphasizing its ability to boost revenue management and increase customer satisfaction. In contrast to conventional pricing strategies, RL allows airlines to adjust prices in real-time by continuously analyzing environmental data such as seat availability, departure time, and competitor pricing. This study explores current pricing models, the framework of RL-driven dynamic pricing, and a case analysis to showcase the real-world advantages and difficulties of RL. Core discoveries reveal that RL-driven dynamic pricing provides considerable benefits in responding to real-time demand fluctuations, thereby optimizing revenue opportunities. Nonetheless, obstacles like limited data, high computational demands, and striking a balance between exploration and exploitation still persist. The research ends with observations on how RL can further reshape airline revenue management and suggests future research avenues to improve its practical uses.