소셜 시뮬레이션에서 많이 적용되는 행위자 행위자 기반 모델을 실제 시스템의 데이터를 바탕으로 최적화하기 위한 기존 연구는 다양한 최적화 기법들을 사용하여 진행되어져 왔다. 그러나, 미래 예측에 적용되는 행위자 기반 시뮬레이션에서는 미래에 대한 시뮬레이션으로서 실제 시스템 데이터가 없으므로 기존의 최적화 방식은 적용하기 힘들며 데이터 동화의 관점에서 에이전트 시뮬레이션 모델을 최적화하는 방법이 필요하다. 본 논문에서는 자가진화 행위자 기반 시뮬레이션 모델의 데이터 동화를 위한 강화학습 방법을 제안하고 이를 실제 주택 시장 모델에 적용하여 유용성을 보이고 있다.
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