Learning Optimal Baggage Routing from Expert Demonstrations

Efficient baggage transportation within baggage handling systems (BHS) is crucial to modern airport logistics, impacting operational efficiency and passenger satisfaction. Optimizing baggage routing is vital to ensure timely delivery. Although reinforcement learning (RL) has shown promise in enhancing baggage routing, it faces significant limitations, including the difficulty of designing suitable reward functions and the risk for suboptimal exploration during training. To overcome these challenges, this study proposes an approach that employs variational Bayesian inverse reinforcement learning (VBIRL) to derive optimal baggage routing policies from expert demonstrations, thereby eliminating the need for manual reward function specification and inefficient exploration in training. A key innovation of our method is the generation of demonstrations using route planning techniques, effectively addressing the lack of expert demonstration data in BHS. Experimental results indicate that our proposed method outperforms conventional RL methods, leading to faster baggage delivery and reduced collision rate, especially in complex BHS.