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English(EN) Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding

新的DMM方法优化多智能体路径寻找协调

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了去中心化主脑（DMM），一种新颖的多智能体路径寻找（MAPF）方法，解决了独立行动采样导致不兼容联合行动的问题。DMM受扩散模型启发，通过通信轮次迭代优化行动意图，使智能体在承诺行动前能够进行协调。该方法采用模仿学习进行训练，并使用MICPO进行优化，与现有的可学习基线相比，在成功率和解决方案成本方面表现更优，在MovingAI任务上实现了高覆盖率，并可扩展到超过一百万个智能体。 AI

影响 这项研究有望提高复杂多智能体系统中的协调性和效率，可能对机器人和自主系统产生影响。

排序理由 该集群描述了一篇详细介绍多智能体路径寻找新方法的最新研究论文。

在 Hugging Face Daily Papers 阅读 →

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新的DMM方法优化多智能体路径寻找协调

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报道来源 [2]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Alexey Skrynnik ·

    去中心化主脑：多智能体寻路中的迭代意图去噪联合行动精炼

    Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies trained on expert data provide an effective approach to this problem. However, when several coordinated jo…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    去中心化主脑：多智能体寻路中的迭代意图去噪联合行动精炼

    Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies trained on expert data provide an effective approach to this problem. However, when several coordinated jo…

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