Data Assimilation Technique for Social Agent-Based Simulation by using Reinforcement Learning

被引：0

作者：

Kang, Dong-oh ^{[1
]}

Bae, Jang Won ^{[1
]}

Lee, Chunhee ^{[1
]}

Jung, Joon-Young ^{[1
]}

Paik, Euihyun ^{[1
]}

机构：

[1] Elect & Telecommun Res Inst, Smart Data Res Grp, Daejeon, South Korea

来源：

PROCEEDINGS OF THE 2018 IEEE/ACM 22ND INTERNATIONAL SYMPOSIUM ON DISTRIBUTED SIMULATION AND REAL TIME APPLICATIONS (DS-RT) | 2018年

关键词：

data assimilation; agent-based; social simulation; reinforcement learning; hidden Markov model;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：220 / 221

页数：2

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