Elevator Control Using Reinforcement Learning to Select Strategy

Abstract: In this thesis, we investigated if reinforcement learning could be applied on elevator systems to improve performance. The performance was evaluated by the average squared waiting time for the passengers, and the buildings considered were apartment buildings. The problem of scheduling elevator cars is an NP-hard problem, and no optimal solution is known. Therefore, an approach where the system learns a strategy instead of using a heuristic, should be the easiest way to get near an optimal solution. A learning system was constructed, where the system was trained to use the best scheduling algorithm out of five in a given situation, based on the prevailing traffic. The purpose of this approach was to reduce the training time that was required in order to get good performance and to lower the complexity of the system. A simulator was then developed, in which the different algorithms were implemented and tested in four different scenarios, where the size of the building and the number of elevator cars varied. The results generated by the simulator showed that reinforcement learning is a great strategy to use in buildings with 16 floors and three or four elevator cars. However, reinforcement learning did not increase the performance in buildings with 10 floors and two to three elevator cars. A possible reason for this is that the variation in performance between the different scheduling algorithms was too small in these scenarios.