  Revenue management can enable airline corporations to maximize the revenue
generated from each scheduled flight departing in their transportation network
by means of finding the optimal policies for differential pricing, seat
inventory control and overbooking. As different demand segments in the market
have different Willingness-To-Pay (WTP), airlines use differential pricing,
booking restrictions, and service amenities to determine different fare classes
or products targeted at each of these demand segments. Because seats are
limited for each flight, airlines also need to allocate seats for each of these
fare classes to prevent lower fare class passengers from displacing higher fare
class ones and set overbooking limits in anticipation of cancellations and
no-shows such that revenue is maximized. Previous work addresses these problems
using optimization techniques or classical Reinforcement Learning methods. This
paper focuses on the latter problem - the seat inventory control problem -
casting it as a Markov Decision Process to be able to find the optimal policy.
Multiple fare classes, concurrent continuous arrival of passengers of different
fare classes, overbooking and random cancellations that are independent of
class have been considered in the model. We have addressed this problem using
Deep Q-Learning with the goal of maximizing the reward for each flight
departure. The implementation of this technique allows us to employ large
continuous state space but also presents the potential opportunity to test on
real time airline data. To generate data and train the agent, a basic
air-travel market simulator was developed. The performance of the agent in
different simulated market scenarios was compared against theoretically optimal
solutions and was found to be nearly close to the expected optimal revenue.
