Research on Air Ticket Trading Agent Based on Reinforcement Learning

• 1、School of Computer and Information Technology, Beijing Jiaotong University

Abstract：In view of the current problems in the air ticket sales field of the civil aviation industry: the travel needs of price-sensitive passengers, the sales volume of air tickets and the low seating rate of passenger aircraft, this paper builds a new transaction model-without affecting the market environment, by The agent purchases air tickets for some passengers and obtains profits by purchasing tickets at low prices. Due to the characteristics of civil aviation business, ticket prices fluctuate frequently, and artificial intelligence can find some laws that are difficult for humans to find out from the changed prices. Reinforcement learning has shown outstanding results in solving sequential decision-making problems, so it has been widely concerned and applied. Air ticket sales can be regarded as a sequential decision problem from a certain angle. This paper attempts to use DQN in reinforcement learning in a more innovative way, combined with CNN convolutional neural network, and proposes an air ticket sales agent model, which is based on Keras. The framework implements the algorithm model. The model selects the best moment for purchasing orders for purchasing orders. The goal is to maximize the revenue of existing orders. This paper proves the benefits of this model in dealing with this problem through experimental results.

Keywords： Reinforcement learning DQN CNN convolutional neural network intelligent decision-making ticket transaction