Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System
文献类型：期刊论文
| 作者 | Zhang, Xianjie1; Liu, Yu1 ; Li, Wenjun3; Gong, Chen2
|
| 刊名 | SENSORS
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 22期号:20页码:24 |
| 关键词 | smart grid deep reinforcement learning cooperative agents communication causal model estimating ITE variational auto-encoder |
| DOI | 10.3390/s22207785 |
| 通讯作者 | Liu, Yu(yuliu@dlut.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | ENERGY |
| 资助项目 | National Natural Science Foundation of China[61672128] ; Fundamental Research Fund for Central University[DUT20TD107] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000873653200001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Fund for Central University |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50492]  |
| 专题 | 综合信息系统研究中心_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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Singapore Management Univ, Sch Comp & Informat Syst, 81 Victoria St, Singapore 188065, Singapore |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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