상세정보
학술대회
Data Assimilation Technique for Social Agent-Based Simulation by using Reinforcement Learning
- 발행일
- 201810
- 출처
- International Symposium on Distributed Simulation and Real Time Applications (DS-RT) 2018, pp.220-221
- 협약과제
- 18HS3200, 점진적 기계학습 기반 자가진화(Self-Evolving) 에이전트 시뮬레이션을 이용한 사회변화 예측분석 기술 개발, 백의현
- 초록
- This paper presents a data assimilation technique for social agent-based simulation to fit real world data automatically by a reinforcement learning method. We used the hidden Markov model in order to estimate the states of the system during the reinforcement learning. The proposed method can improve simulation models of the social agent-based simulation incrementally when new real data are available without total optimization. In order to show the feasibility, we applied the proposed method to a housing market problem with real Korean housing market data.
- KSP 제안 키워드
- Housing Market, Learning methods, Market Data, Real data, Real-world, Reinforcement Learning(RL), Simulation Model, Social agent, agent-based simulation, data assimilation, hidden Markov Model