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Indigo Multi-agent Online Optimization | Erin Mills Town Centre
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This monograph provides an overview of distributed online optimization in multi-agent systems. Online optimization approaches planning and decision problems from a robust learning perspective, where one learns through feedback from sequentially arriving costs, resembling a game between a learner (agent) and the environment. Recently, multi-agent systems have become important in diverse areas including smart power grids, communication networks, machine learning, and robotics, where agents work with decentralized data, costs, and decisions to collectively minimize a system-wide cost. In such settings, agents make distributed decisions and collaborate with neighboring agents through a communication network, leading to scalable solutions that often perform as well as centralized methods.The monograph offers a unified introduction, starting with fundamental algorithms for basic problems, and gradually covering state-of-the-art techniques for more complex settings. The interplay between individual agent learning rates, network structure, and communication complexity is highlighted in the overall system performance.
This monograph provides an overview of distributed online optimization in multi-agent systems. Online optimization approaches planning and decision problems from a robust learning perspective, where one learns through feedback from sequentially arriving costs, resembling a game between a learner (agent) and the environment. Recently, multi-agent systems have become important in diverse areas including smart power grids, communication networks, machine learning, and robotics, where agents work with decentralized data, costs, and decisions to collectively minimize a system-wide cost. In such settings, agents make distributed decisions and collaborate with neighboring agents through a communication network, leading to scalable solutions that often perform as well as centralized methods.The monograph offers a unified introduction, starting with fundamental algorithms for basic problems, and gradually covering state-of-the-art techniques for more complex settings. The interplay between individual agent learning rates, network structure, and communication complexity is highlighted in the overall system performance.
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