Abstract

A novel kNN-based approximation method for Reinforcement Learning (RL) is used to control and optimize a Discrete Event Simulation (DES). The method does not require design parameters, is suitable for unknown and new simulation environments, and can handle irregular and partially sparse state space. We show in a demonstrative queuing simulation that the method is more robust than artificial neural networks and achieves comparable performance.