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English(EN) Bayesian Offline Multi-Agent Reinforcement Learning with Latent World Models

博弈论与强化学习在新的研究论文中正式关联

作者 PulseAugur 编辑部 · [5 个来源] ·

两篇新学术论文探讨了博弈论与强化学习之间的基础联系。第一篇论文《游戏何时是马尔可夫决策过程？》（作者：Eduardo C. Garrido-Merchán）提供了正式定理，展示了诸如纳什均衡和Shapley值等各种博弈论概念如何直接对应于强化学习中的结构和算子。第二篇论文《强化学习的统计基础：非渐近视角》侧重于强化学习的统计基础，特别是在样本稀疏的情况下，并对表格型马尔可夫决策过程进行了非渐近处理。 AI

影响 这些论文通过将强化学习与已建立的博弈论概念正式联系起来，提供了对强化学习更深入的理论理解，有可能带来更强大、更高效的算法。

排序理由 两篇在arXiv上发表的关于理论研究的学术论文。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →

博弈论与强化学习在新的研究论文中正式关联

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报道来源 [5]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jonathan Lawry ·

    An Axiomatic Framework for Reinforcement Learning in Open Multi-Agent Systems

    Open multi-agent systems, where only a subset of agents are controllable, are central to real-world domains such as smart grids, yet lack a principled foundation for reinforcement learning algorithm design. We study this setting through n-agent ad hoc teamwork (NAHT), a recent fr…

  2. arXiv cs.LG TIER_1 English(EN) · Eduardo C. Garrido-Merch\'an ·

    When Is a Game a Markov Decision Process? Correspondences Between Game Theory and Reinforcement Learning

    arXiv:2610.41962v1 Announce Type: cross Abstract: Reinforcement learning and game theory describe the same object from two sides. Game theory says what a rational outcome of a strategic interaction is; reinforcement learning says how an agent could reach one through experience. T…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Ersha Li ·

    From Single-Agent Self-Discovery to Multi-Agent Adaptation: Scaling Laws of Partner Feedback and Convention Rigidity in Meta Reinforcement Learning Algorithms

    A key milestone toward Recursive Self-Improvement (RSI) is replacing hand-crafted optimizers with meta-learned update rules (such as Disco103 and OPEN) that discover how agents learn. Yet when a self-discovered RL model trains with adapting teammates, does co-evolution sustain ca…

  4. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jakob Foerster ·

    Bayesian Offline Multi-Agent Reinforcement Learning with Latent World Models

    Model-based offline multi-agent reinforcement learning (MARL) provides a promising approach to multi-agent decision-making under partial observability, where the global state is unavailable from offline data. However, limited dataset coverage can lead to substantial out-of-distri…

  5. arXiv stat.ML TIER_1 English(EN) · Yuxin Chen, Yuejie Chi, Jianqing Fan, Gen Li, Yuting Wei, Yuling Yan ·

    Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective

    arXiv:2610.41903v1 Announce Type: new Abstract: Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty. The rapid growth of model complexity in emerging applications has significantly amplified the challenges of achi…

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