Joint learning of agents and graph embeddings in a conveyor belt control problem

We focus on the problem of routing a conveyor belts system based on a multi-agent approach. Most of these airport baggage belt conveyor systems use routing algorithms based on manual simulation of conveyor behavior. This approach does not scale well, and new research in machine learning proposes to solve the routing problem using reinforcement learning. To solve this problem, we propose an approach to joint learning of agents and vector representations of a graph. Within this approach, we develop a QSDNE algorithm, which uses DQN agents and SDNE embeddings. A comparative analysis was carried out with multi-agent routing algorithms without joint learning. The results of the QSDNE algorithm showed its effectiveness in optimizing the delivery time and energy consumption in conveyor systems as it helped to reduce mean delivery time by 6 %. The proposed approach can be used to solve routing problems with complex path estimation functions and dynamically changing graph topologies, and the proposed algorithm can be used to control conveyor belts at airports and in manufacturing workshops.