Reinforcement Learning for Assignment problem

On Demand services, such as a ride sharing [1], coordination of multiply robots [2], user serving in MIMO networks [3] etc utilize management strategies in order to improve customer quality of service (QoS) requirements. The problem of shared resource utilization is very common in wireless networks [4] and becoming more important with more devices connected because of development of IoT and 5G. Usually such systems have multiply concurrent users awaiting serving and fewer number of workers resources available, along with switching costs from serving user to user (like trip for taxi driver from drop off of one user to pick up point of the next one). Real world systems are dynamic in nature with cause and effect information not being given and system behavior and QoS only being observed. Previous works developed different algorithmic or classical scheduling methods, where QoS is maintained via algorithm using some sort of priority index, like Proportional Fair [5], [3] or MLWDF [6]. This work focuses on reinforced learning applied to general formulation of user scheduling problem. A Q-learning based method is presented for maximizing customer QoS and compared to analytical strategies. A Q-learning approach is shown to improve QoS up to TODO% compared to baseline scenarios.