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A Deep Reinforcement Learning Approach to Dynamic Airline Ticket Pricing and Customer Response Analysis
Abstract
In the ever-changing landscape of airline ticket sales, efficient dynamic pricing strategies are crucial for maximizing revenue while catering to diverse customer preferences. This paper explores the application of Deep Reinforcement Learning (DRL) algorithms, namely REINFORCE, PPO, A2C, SAC, and TD3, in the context of airline ticket pricing. Leveraging a synthetic dataset and a Generalized Linear Model, these algorithms were rigorously evaluated. Our study reveals that TD3 outperforms other models, showcasing rapid convergence and robust reward optimization capabilities. We also provide a comparative analysis of training times, essential for practical implementation. Through extensive experimentation and computational analysis, this research contributes valuable insights into the efficacy of DRL techniques in dynamic pricing. The findings not only offer benchmarks for airline industry applications but also illuminate the broader potential of advanced machine learning methods in revenue management across various sectors. This study underscores the pivotal role of artificial intelligence in shaping the future of pricing strategies, providing a roadmap for businesses aiming to stay competitive in today`s dynamic markets.
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