English(EN) Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices

新的学习动态在一般和博弈中实现恒定交换遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员为多人一般和博弈开发了新的确定性和非耦合学习动态。这些动态实现了每个玩家的恒定个体交换遗憾，独立于博弈的周期。该方法涉及玩家预测偏差收益以更新转移矩阵并玩平稳分布，证明利用了势能论证和高阶预测分析。使用公共前缀切换包装器的对抗性变体在对抗性环境中保持自玩界限并保证个体交换遗憾。 AI

影响这项研究可以为开发更强大的 AI 代理提供信息，这些代理能够在复杂的多代理环境中进行战略决策。

排序理由该集群包含一篇详细介绍博弈论新理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍博弈论新理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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arXiv cs.LG TIER_1 English(EN) · Tung Mai · 2026-09-16 04:00

通过乐观转移矩阵在一般和博弈中实现持续的交换后悔

arXiv:2609.16751v1 Announce Type: cross Abstract: We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon $T$. With $n$ players and at m…