Song, BingBingSongXiong, GangGangXiongYu, SongminSongminYuYe, PeijunPeijunYeDong, XisongXisongDongLv, YishengYishengLv2022-03-152022-03-152021https://publica.fraunhofer.de/handle/publica/41254510.1109/DTPI52967.2021.9540180In the research and application of Agent-Based Models (ABM), parameter calibration is an important content. Based on the existing state transfer equations that link the micro-parameters and macro-states of the multi-agent system, this paper further proposes to introduce Reinforcement Learning when calibrating the parameters. The state transfer of the agent after learning is used to calibrate the micro-parameters of ABM, and the interaction between each agent and multiple other agents is expressed as the parameters of the agent. The application case study of population migration demonstrates that our method can achieve high accuracy and low computational complexity.enagent based modelreinforcement learningcalibration303600conference paper