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English(EN) GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning

新AI系统GARDEN实现多智能体学习的奖励设计自动化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了GARDEN，一个用于合作多智能体强化学习中自主奖励设计的新颖系统。GARDEN旨在创建一个单一的奖励程序，该程序可以有效地跨一系列相关场景重复使用，从而减少手动奖励工程的需求。该系统调查环境代码、训练记录和智能体行为，使用LLM奖励工程师来开发和测试奖励。在Overcooked和SMAX基准测试中的测试表明，与默认奖励和其他方法相比，GARDEN显著提高了任务回报和胜率。 AI

影响 为多智能体系统实现奖励函数设计的自动化，有可能加速复杂合作任务的开发并提高其性能。

排序理由 这是一篇详细介绍AI奖励设计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新AI系统GARDEN实现多智能体学习的奖励设计自动化

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这是一篇详细介绍AI奖励设计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dogan Urgun ·

    GARDEN：通过证据导航实现通用自主奖励设计，用于合作多智能体强化学习

    arXiv:2610.37926v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because …

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