How to solve a classification problem using a cooperative tiling Multi-Agent System?
Institut de Recherche en Informatique de Toulouse（图卢兹计算机科学研究所）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）；Toulouse INP（图卢兹国立理工学院）；UT3（图卢兹第三大学）；UT1（图卢兹第一大学）；Citec Ingénieurs Conseil（赛泰克工程咨询公司）
Thibault Fourez, Nicolas Verstaevel, Frédéric Migeon, Frédéric Schettini, Frédéric Amblard
备注：
20th International Conference on Practical Applications of Agents and Multi-Agent Systems, Jul 2022, L'Aquila, Italy. arXiv admin note: substantial text overlap with arXiv:2209.06824
Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems of local cooperation between agents. We present smapy, an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for online non-linear classification on three benchmark toy problems chosen for their different levels of linear separability, if they are integrated in a cooperative Multi-Agent structure. The results obtained show a significant improvement of the performance of linear classifiers in non-linear contexts in terms of classification accuracy and decision boundaries, thanks to the cooperative approach.
McGill University（麦吉尔大学）；Mila；Université de Montréal（蒙特利尔大学）；DeepMind
在本文中，我们研究学习时间抽象的合作多智能体系统，使用的选项框架（萨顿等，1999年）。首先，我们解决的分散POMDP的多智能体系统所代表的规划问题，通过引入一个\emph{common information approach}。我们使用\emph{common beliefs}和广播的概念来解决一个等价的集中式POMDP问题。然后，我们提出了分布式期权评价（DOC）算法，它使用集中式期权评价和分散式期权内改进。我们从理论上分析了DOC的渐近收敛性，并建立了一个新的多智能体环境，以证明其有效性。我们的实验经验表明，DOC执行竞争力对基线和规模的代理商的数量。
Pacific Northwest National Laboratory（太平洋西北国家实验室）；The City College of New York（纽约市立学院）；Grove School of Engineering（格罗夫工程学院）；City University of New York（纽约城市大学）
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective
Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；School of Mechanical Engineering and Automation（机械工程与自动化学院）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
Improved Hierarchical ADMM for Nonconvex Cooperative Distributed Model Predictive Control
National University of Singapore（新加坡国立大学）；University of California, Berkeley（加州大学伯克利分校）；Temasek Laboratories, National University of Singapore（新加坡国立大学淡马锡实验室）；University of British Columbia（不列颠哥伦比亚大学）
