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English(EN) Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings

研究人员开发了具有不同奖励塑造的多智能体AI的零样本协调

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种用于多智能体强化学习中零样本协调（ZSC）的新方法，使智能体即使在奖励信号被不同地塑造时也能与未知伙伴有效协作。该方法包括使用四种不同算法选择的随机奖励塑造来训练一组方法。在Overcooked环境中的实验显示出显著的改进，与基线ZSC算法相比，稀疏奖励增加了62.2%至119.2%。 AI

影响 提高了稀疏奖励设置下的多智能体协调能力，有可能增强复杂协作任务的性能。

排序理由 关于一种新颖强化学习技术的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

研究人员开发了具有不同奖励塑造的多智能体AI的零样本协调

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Keenan Powell, Peihong Yu, Pratap Tokekar ·

    具有多样化奖励塑造的稀疏奖励任务的零样本协调

    arXiv:2604.25076v1 Announce Type: new Abstract: Many Multi-Agent Reinforcement Learning (MARL) agents fail to adapt properly to cooperating with agents trained with the same objectives but different seeds, algorithms, or other training differences. This is the problem of Zero-Sho…

  2. arXiv cs.LG TIER_1 English(EN) · Pratap Tokekar ·

    具有多样化奖励塑造的稀疏奖励任务的零样本协调

    Many Multi-Agent Reinforcement Learning (MARL) agents fail to adapt properly to cooperating with agents trained with the same objectives but different seeds, algorithms, or other training differences. This is the problem of Zero-Shot Coordination (ZSC), which focuses on training …

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