Calibration of Agent-Based Model Using Reinforcement Learning
文献类型：会议论文
| 作者 | Song B(宋冰)1,3 ; Xiong G(熊刚)1,3; Yu S（于松民）2; Ye P（叶佩军）1,3; Dong X（董西松）1,3; Lv Y（吕宜生）1,3
|
| 出版日期 | 2021 |
| 会议日期 | 2021 |
| 会议地点 | Beijing |
| 英文摘要 | In the research and application of Agent-based
Models , 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
/earning 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. |
| 会议录出版者 | IEEE |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52158]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Ye P（叶佩军） |
| 作者单位 | 1.School of Artificial , intelligence university of chinese Academy of Sciences 2.Fraunhfer , institute for Systems and , innovation Research 3.The State . key Laboratory for Management and control of complex Systems , institute of Automation chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Song B,Xiong G,Yu S,et al. Calibration of Agent-Based Model Using Reinforcement Learning[C]. 见:. Beijing. 2021. |
入库方式： OAI收割
来源：自动化研究所
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