Reinforcement learning applied to airline revenue management

Reinforcement learning (RL) is an area of machine learning concerned with how agents take actions to optimize a given long-term reward by interacting with the environment they are placed in. Some well-known recent applications include self-driving cars and computers playing games with super-human performance. One of the main advantages of this approach is that there is no need to explicitly model the nature of the interactions with the environment. In this work, we present a new airline Revenue Management System (RMS) based on RL, which does not require a demand forecaster. The optimization module remains but works in a different way. It is theoretically proven that RL converges to the optimal solution; however, in practice, the system may require a significant amount of data (a booking history with millions of daily departures) to learn the optimal policies. To overcome these difficulties, we present a novel model that integrates domain knowledge with a deep neural network trained on GPUs. The results are very encouraging in different scenarios and open the door for a new generation of RMSs that could automatically learn by directly interacting with customers.