English(EN) Learning vs. Optimizing Players in Budgeted Games

新研究探讨预算博弈中的学习与优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一篇新研究论文探讨了带预算约束的重复一般博弈，重点关注效用最大化优化器与学习代理之间的交互。该研究引入了新颖的校准方法，以确保学习者的不可操纵性和低随机遗憾，即使预算使跨轮决策复杂化。所提出的学习算法通过最佳响应优化器策略的校准预测来实现这些目标，这些预测是通过在线梯度上升模拟构建的。 AI

影响这项研究可能为在复杂、资源受限环境中进行战略交互的更鲁棒的AI代理的开发提供信息。

排序理由该条目是发表在arXiv上的研究论文，详细介绍了博弈论的理论工作。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Giannis Fikioris, Balasubramanian Sivan, \'Eva Tardos · 2026-11-05 05:00

学习 vs. 优化预算博弈中的玩家

arXiv:2611.05580v1 Announce Type: cross Abstract: We study repeated general games over $T$ rounds between a utility-maximizing optimizer and a learning agent, where budget constraints couple the players' decisions across rounds. Without budgets, low swap regret simultaneously gua…