MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets
Department of Mechanical Engineering, Universidad de los Andes（机械工程系，洛斯安第斯大学）；Department of Industrial Engineering, Universidad de los Andes（工业工程系，洛斯安第斯大学）
Nelson Salazar-Pena, Alejandra Tabares, Andres Gonzalez-Mancera
This paper introduces a novel, open-source MARL simulation framework for studying implicit cooperation in LEMs, modeled as a decentralized partially observable Markov decision process and implemented as a Gymnasium environment for MARL. Our framework features a modular market platform with plug-and-play clearing mechanisms, physically constrained agent models (including battery storage), a realistic grid network, and a comprehensive analytics suite to evaluate emergent coordination. The main contribution is a novel method to foster implicit cooperation, where agents' observations and rewards are enhanced with system-level key performance indicators to enable them to independently learn strategies that benefit the entire system and aim for collectively beneficial outcomes without explicit communication. Through representative case studies (available in a dedicated GitHub repository in https://github.com/salazarna/marlem, we show the framework's ability to analyze how different market configurations (such as varying storage deployment) impact system performance. This illustrates its potential to facilitate emergent coordination, improve market efficiency, and strengthen grid stability. The proposed simulation framework is a flexible, extensible, and reproducible tool for researchers and practitioners to design, test, and validate strategies for future intelligent, decentralized energy systems.
Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets
Department of Mechanical Engineering, Universidad de los Andes（机械工程系，andes大学）；Department of Industrial Engineering, Universidad de los Andes（工业工程系，andes大学）
PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
Doctoral School of Exact and Natural Sciences, Jagiellonian University（杰哥利昂大学精确与自然科学博士学院）；Faculty of Mathematics and Computer Science, Jagiellonian University（杰哥利昂大学数学与计算机科学学院）
Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Cyber Engineering, Xidian University（西安电子科技大学网络空间安全学院）；Yichang Testing Technology Research Institute（宜昌测试技术研究所）；Department of Informatics and Telecommunications, National and Kapodistrian University of Athens（希腊雅典国家与kapodistrian大学信息与电信系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；KU 6G Research Center, Department of Computer and Information Engineering, Khalifa University（阿布扎比哈利法大学计算机与信息工程系6G研究中心）；Department of Computer Science and Engineering, Kyung Hee University（韩国庆熙大学计算机科学与工程系）
RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
IBM T. J. Watson Research Center（IBM T. J. 沃森研究中心）；Princeton University（普林斯顿大学）；Massachusetts Institute of Technology（麻省理工学院）；Northeastern University（东北大学）
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）
Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning
Laboratory for Information & Decision Systems, Massachusetts Institute of Technology（信息与决策系统实验室，麻省理工学院）；Harvard University（哈佛大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
