Knowledge Commons of Institute of Automation,CAS
Calibration of Agent-Based Model Using Reinforcement Learning | |
Song B(宋冰)1,2![]() ![]() ![]() ![]() ![]() | |
2021 | |
会议名称 | 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) |
会议日期 | 2021 |
会议地点 | Beijing |
出版者 | IEEE |
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. |
收录类别 | EI |
七大方向——子方向分类 | 多智能体系统 |
国重实验室规划方向分类 | 多智能体决策 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52158 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Ye P(叶佩军) |
作者单位 | 1.The State . key Laboratory for Management and control of complex Systems , institute of Automation chinese Academy of Sciences 2.School of Artificial , intelligence university of chinese Academy of Sciences 3.Fraunhfer , institute for Systems and , innovation Research |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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]:IEEE,2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Calibration_of_Agent(437KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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