论文标题
多代理系统中的分布式无重格学习
Distributed No-Regret Learning in Multi-Agent Systems
论文作者
论文摘要
在这篇教程文章中，我们概述了以重复未知游戏为模型的多代理系统中的分布式无regret学习的新挑战和代表性结果。探索了四个新兴游戏特征 - - 动态性，不完整和不完美的反馈，有限的理性和异质性 - 探索了挑战规范游戏模型。对于四个特征中的每个特征，我们都阐明了它在游戏建模，遗憾的概念，可行的游戏结果以及分布式学习算法的设计和分析中的影响和影响。
In this tutorial article, we give an overview of new challenges and representative results on distributed no-regret learning in multi-agent systems modeled as repeated unknown games. Four emerging game characteristics---dynamicity, incomplete and imperfect feedback, bounded rationality, and heterogeneity---that challenge canonical game models are explored. For each of the four characteristics, we illuminate its implications and ramifications in game modeling, notions of regret, feasible game outcomes, and the design and analysis of distributed learning algorithms.
