Reinforcement Learning for Elevator Control ?

Reinforcement learning (RL) comprises an array of techniques that learn a control policy so as to maximize a reward signal. When applied to the control of elevator systems, RL has the potential of finding better control policies than classical heuristic, suboptimal policies. On the other hand, elevator systems offer an interesting benchmark application for the study of RL. In this paper, RL is applied to a single-elevator system. The mathematical model of the elevator system is described in detail, making the system easy to re-implement and re-use. An experimental comparison is made between the performance of the Q-value iteration and Q-learning RL algorithms, when applied to the elevator system.