Generating inspiration for multi-agent simulation design by Q-Learning
Robert Junges, Franziska Klügl · 2010
Abstract—One major challenge in developing multi-agent simulations is to find the appropriate agent design that is able generating the intended overall phenomenon respectively dynamics, but does not contain unnecessary details. In this paper we suggest to use agent learning for supporting the development of an agent model: The modeler defines the environmental model and the agent interfaces. Using rewards capturing the intended agent behavior, Reinforcement Learning techniques can be used for learning the rules that are optimally governing the agent behavior. However, for really being useful in a modeling and simulation context, a human modeler must be able to review and understand the outcome of the learning. We propose to use additional forms of learning as post-processing step for supporting the analysis of the learnt model. We test our ideas using a simple evacuation simulation scenario. I.