Distributed Station Assignment Through Learning

We study a dynamic assignment problem in a communication network where a set of mobile nodes called clients have to upload data packets to a set of static nodes called stations. Clients are restricted by a maximum delay between packet uploads, and stations by a maximum capacity at any given round of communication. The goal is to assign clients to stations in each round aiming to minimize various energy-related costs. The Station Assignment problem has been well studied from a centralized scheduling perspective. In this work we consider the application of Multi-Agent Reinforcement Learning (MARL) so that each client can self-assign to a station independently using only local information. MARL is challenging because the decisions of clients are affected by the concurrent decisions of other clients. In this work, as a step towards a better understanding of the feasibility of MARL for distributed assignment problems, we study Distributed Station Assignment experimentally applying Independent Proximal Policy Optimization (IPPO) for various scenarios.