A Bayesian Reinforcement Learning Algorithm Based on Abstract States for Elevator Group Scheduling Systems

In order to solve the curse of dimensionality problem encountered by reinforcement learning algorithms for Elevator group scheduling (EGS) systems with large-scale state space, a kind of Bayesian reinforcement learning algorithm based on Abstract states (BRL-AS) was proposed. On one hand, an abstract state space whose size is much smaller than that of the original state space was constructed by analyzing the motion situations of EGS systems. On the other hand, a Bayesian network was used to carry out an inference operation on the abstract states and to obtain discrete real-valued variables, which is not only suitable for numerical computation of neural networks, but also can further reduce the size of the state space. The neural network model used for value-function approximating based on the inference output of the Bayesian network not only can solve the problem of continuous space expression of reinforcement learning system, but also can improve the system learning speed due to its simple topology structure. Simulation results of an EGS system for typical traffic profiles verify the feasibility and validity of the proposed reinforcement learning scheduling algorithm.