In various real-world scenarios, interactions among agents often resemble the
dynamics of general-sum games, where each agent strives to optimize its own
utility. Despite the ubiquitous relevance of such settings, decentralized
machine learning algorithms have struggled to find equilibria that maximize
individual utility while preserving social welfare. In t
本文讨论了基于网络化多智能体系统的分散在线凸优化，并提出了一种新的算法 —— 学习增强的分散式在线优化（LADO），使个体代理人仅基于本地在线信息选择动作。与现有的集中式学习增强在线算法形成鲜明对比，LADO 实现了分散式设置下的强大的鲁棒性保证。我们还证明了 LADO 的平均成本限制，揭示了平均性能和最坏情况下鲁棒性之间的权衡，并表明通过明确考虑鲁棒性要求来训练 ML 策略的优势。