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

共 50 条

[41] A methodological framework for the integration of machine learning algorithms into agent-based simulation [J].

Zornic, Nikola ;

Markovic, Aleksandar .

JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 2022, 28 (05) :540-562

[42] Large-scale agent-based modelling of street robbery using graphical processing units and reinforcement learning [J].

Joubert, Christiaan J. ;

Saprykin, Aleksandr ;

Chokani, Ndaona ;

Abhari, Reza S. .

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2022, 94

[43] A hierarchical agent-based approach to simulate a dynamic decision-making process of evacuees using reinforcement learning [J].

Hassanpour, Sajjad ;

Rassafi, Amir Abbas ;

Gonzalez, Vicente A. ;

Liu, Jiamou .

JOURNAL OF CHOICE MODELLING, 2021, 39

[44] A Reinforcement learning algorithm for Agent-based Computational Economics (ACE) model of electricity markets [J].

Radhakrishnan, Bharat Menon ;

Srinivasan, Dipti ;

Lau, Yong Fu Alfred ;

Parasumanna, Balaji Gokulan ;

Rathore, Akshay Kumar ;

Panda, Sanjib Kumar ;

Khambadkone, Ashwin .

2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2015, :297-303

[45] Multi-robot systems with agent-based reinforcement learning: evolution, opportunities and challenges [J].

Yang, Erfu ;

Gu, Dongbing .

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2009, 6 (04) :271-286

[46] Building occupancy simulation and data assimilation using a graph-based agent-oriented model [J].

Rai, Sanish ;

Hu, Xiaolin .

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 502 :270-287

[47] Distributed agent-based deep reinforcement learning for large scale traffic signal control [J].

Wu, Qiang ;

Wu, Jianqing ;

Shen, Jun ;

Du, Bo ;

Telikani, Akbar ;

Fahmideh, Mahdi ;

Liang, Chao .

KNOWLEDGE-BASED SYSTEMS, 2022, 241

[48] Reinforcement Learning DDPG–PPO Agent-Based Control System for Rotary Inverted Pendulum [J].

Rajmeet Singh Bhourji ;

Saeed Mozaffari ;

Shahpour Alirezaee .

Arabian Journal for Science and Engineering, 2024, 49 :1683-1696

[49] Task Degradation in Agent-Based Simulation [J].

Rice, Daniel ;

Andra, Mitha .

MILITARY MODELING & SIMULATION SYMPOSIUM 2011 (MMS 2011) - 2011 SPRING SIMULATION MULTICONFERENCE - BK 7 OF 8, 2011, :19-22

[50] Tutorial on agent-based modelling and simulation [J].

Macal, C. M. ;

North, M. J. .

JOURNAL OF SIMULATION, 2010, 4 (03) :151-162

← 1 2 3 4 5 →