Strategy Selection by Reinforcement Learning for Multi-car Elevator Systems

This paper discusses the group control of elevators for improving efficiency, an efficient control method for multi-car elevator using reinforcement learning is proposed. In the method, the control agent selects the best strategy among four strategies, namely Transportation strategy, Passenger strategy, Zone strategy, and Difference strategy according to traffic flow. The control agent takes the number of total passengers and the distance from the departure floor to the destination floor of a call into account. Through experiments, the performance of the proposed method is shown, the average service time of the proposed method is compared with the average service time obtained for the cases where the car assignment is made by each of the three or four strategies.