Elevator group control is a multi-objective optimization problem in an open

complicated and dynamical system.Currently

many algorithms have been applied in elevator group control

such as zoning approaches

search-based approaches

rule-based approaches and other adaptive approaches.However these methods fail of achieving the optimal performance in the average wait time.In this paper

the reinforcement learning technology is applied in the elevator group control system.The CMAC neural network is used to approx the value function of reinforcement learning and Q-learning algorithm is used to optimize the value function

thereby the optimal control policy of the elevator group control is achieved.The simulation experiment shows that the elevator group control using reinforcement learning with CMAC can reduce the average wait time efficiently in the down peak traffic.