Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System

doi:10.3390/s22207785

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 脑机融合与认知评估

	Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System
	Zhang, Xianjie 1; Liu, Yu1 ; Li, Wenjun 2; Gong, Chen 3
发表期刊	SENSORS
	2022-10-01
卷号	22 期号:20 页码:24
通讯作者	Liu, Yu(yuliu@dlut.edu.cn)
摘要	Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system's coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem is to build mechanisms to selectively send out communications, which allow distributors to send information at certain moments and key states. The distributors in the power grid are modeled as reinforcement learning agents, and the communication bandwidth in the power grid can be reduced by optimizing the communication frequency between agents. Therefore, in this paper, we propose a model for deciding whether to communicate based on the causal inference method, Causal Inference Communication Model (CICM). CICM regards whether to communicate as a binary intervention variable, and determines which intervention is more effective by estimating the individual treatment effect (ITE). It offers the optimal communication strategy about whether to send information while ensuring task completion. This method effectively reduces the communication frequency between grid distributors, and at the same time maximizes the power distribution effect. In addition, we test the method in StarCraft II and 3D environment habitation experiments, which fully proves the effectiveness of the method.
关键词	smart grid deep reinforcement learning cooperative agents communication causal model estimating ITE variational auto-encoder
DOI	10.3390/s22207785
关键词[WOS]	ENERGY
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61672128] ; Fundamental Research Fund for Central University[DUT20TD107]
项目资助者	National Natural Science Foundation of China ; Fundamental Research Fund for Central University
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000873653200001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50493
专题	多模态人工智能系统全国重点实验室_脑机融合与认知评估
通讯作者	Liu, Yu
作者单位	1.Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116620, Peoples R China 2.Singapore Management Univ, Sch Comp & Informat Syst, 81 Victoria St, Singapore 188065, Singapore 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xianjie,Liu, Yu,Li, Wenjun,et al. Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System[J]. SENSORS,2022,22(20):24.
APA	Zhang, Xianjie,Liu, Yu,Li, Wenjun,&Gong, Chen.(2022).Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System.SENSORS,22(20),24.
MLA	Zhang, Xianjie,et al."Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System".SENSORS 22.20(2022):24.