Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations. To automate this process and relieve engineers' burden, a novel multi-stage control approach is presented in this paper to train centralized and decentralized reinforcement learning agents that can automatically adjust grid controllers for regulating transmission line flows at normal condition and under contingencies. The power grid flow control problem is formulated as Markov Decision Process (MDP). At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits. If line overloading issues remain unresolved, stage two is used to train decentralized SAC agent via load throw-over at local substations. The effectiveness of the proposed approach is verified on a series of actual planning cases used for operating the power grid of SGCC Zhejiang Electric Power Company.
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Zhejiang University（浙江大学）；Zhejiang Provincial Engineering Research Center for Real-Time SmartTech in Urban Security Governance, School of Computer and Computing Science, Hangzhou City University（浙江省实时智慧城市治理实时智能技术工程研究中心，计算机与计算科学学院，杭州市大学）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）
Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management
organization= Department of Civil；Environmental Engineering , addressline= The Pennsylvania State University , city= University Park , postcode= 16802 , state= PA , country= USA；organization= Faculty of Architecture \& the Built Environment , addressline= Delft University of Technology , postcode= 2628 BL , state= Delft , country= The Netherlands
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
University of Rochester（罗切斯特大学）；ETH Zurich（苏黎世联邦理工学院）；University of California, Davis（加利福尼亚大学戴维斯分校）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Tencent AI lab（腾讯人工智能实验室）
Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
1 Centre Inria d'Université Côte d'Azur, INRIA, Sophia Antipolis, France.；2 Laboratoire GAATI, Universit\' e de la Polyn\' e sie fran c aise, Fa`a`\= a , French Polynesia.；3 School of Electrical；Electronic Engineering, University of Sheffield, Sheffield, United Kingdom.；4 ECE Dept. Princeton University, Princeton, 08544 NJ, USA.
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Tsinghua University（清华大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究所（TeleAI），中国电信）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiaotong University（上海交通大学）
DeceFL: A Principled Decentralized Federated Learning Framework
Huazhong University of Science and Technology（华中科技大学）；University of Waterloo（滑铁卢大学）；Northeastern University（东北大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
University of Rochester（罗切斯特大学）；University of Michigan（密歇根大学）；Kwai Inc（快手公司）；ETH Zurich（苏黎世联邦理工学院）；Hong Kong University of Science and Technology（香港科技大学）
Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series
Department of Biomedical Engineering, PolyU（PolyU 生物医学工程系）；School of Nursing, PolyU（PolyU 护理学院）；Yau Mathematical Sciences Center, Tsinghua University（清华大学尤太数学科学中心）
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Aalborg University（奥尔堡大学）；University of Twente（特温特大学）；Netherlands Defence Academy（荷兰国防学院）；Delft University of Technology（代尔夫特理工大学）
DeMuon: A Decentralized Muon for Matrix Optimization over Graphs
Department of Mathematics, Linköping University（利乌普堡大学数学系）；Department of Electrical Engineering, Linköping University（利乌普堡大学电气工程系）；Department of Computer and Information Science, Linköping University（利乌普堡大学计算机与信息科学系）
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application
Department of Computer Science Mahishadal Raj College, Mahishadal, West Bengal, 721628, India；Distributed Systems (CLOUDS) Laboratory, \ of Computing；Information Systems, The University of Melbourne, Victoria, 3052, Australia
Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Wake Forest University（维克森林大学）；Shandong University（山东大学）；University of Science and Technology of China（中国科学技术大学）
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration
College of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间科学与技术学院）
Unveiling the Power of Multiple Gossip Steps: A Stability-Based Generalization Analysis in Decentralized Training
State Key Laboratory of Digital Intelligent Modeling and Simulation（数字智能建模与仿真国家重点实验室）；National University of Defense Technology（国防科技大学）；School of Cyber Science and Technology（网络安全科学与技术学院）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；PKU-OCTA Laboratory for Blockchain and Privacy Computing（区块链与隐私计算实验室）；National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
