Fleet Planning Under Demand Uncertainty: A Reinforcement Learning Approach

This work proposes a model-free reinforcement learning approach to learn a long-term fleet planning problem subjected to air-travel demand uncertainty. The aim is to develop a dynamic fleet policy which adapts over time by intermediate assessments of the states. A Deep Q-network is trained to estimate the optimal fleet decisions based on the...