基于接地联合注意力的他人玩法用于零样本协调
Grounded Joint-Attention Other-Play for Zero-Shot Coordination
- University of Stuttgart（斯图加特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出MATE，一种受人类联合注意力启发的多智能体强化学习方法，通过对齐视觉注意力促进零样本协调，在多个基准上优于对称性破缺方法。
AI中文摘要：
联合注意力——人类与他人共享共同视觉或认知焦点的能力——促成了心灵的相遇，使我们即使与不熟悉的伙伴也能协调行动。在本工作中，我们研究是否赋予AI智能体类似机制能够实现这种零样本协调。我们引入了用于零样本团队协作的相互注意力（MATE）：一种受人类联合注意力启发的新型多智能体强化学习方法。MATE鼓励智能体通过将视觉注意力对齐到场景显著物体上来协调其动作，而不是依赖训练期间形成的任意伙伴依赖的约定。与仅防止脆弱约定出现的对称性破缺方法不同，MATE通过一种在伙伴间自然共享的基于环境的信号主动促进协调。我们在三个基准上评估MATE：我们的卡片对齐游戏（旨在隔离脆弱约定的形成），以及更具挑战性的基于级别的觅食和OvercookedV2基准。我们的实验一致表明，受联合注意力启发的信号改善了与未知伙伴的协调，突显了MATE作为通用协调机制的潜力，该机制补充并超越了对称性破缺方法。
英文摘要：
Joint attention - the human ability to share a common visual or cognitive focus with others - enables a meeting of minds that lets us coordinate even with unfamiliar partners. In this work we investigate whether equipping AI agents with a similar mechanism can enable such zero-shot coordination. We introduce Mutual Attention for zero-shot TEaming (MATE): a novel multi-agent reinforcement learning method inspired by human joint attention. MATE encourages agents to coordinate their actions by aligning their visual attention on scene-salient objects during the interaction rather than relying on arbitrary partner-dependent conventions established during training. Unlike symmetry-breaking approaches that merely prevent brittle conventions from emerging, MATE actively promotes coordination through an environment-grounded signal that is naturally shared across partners. We evaluate MATE on three benchmarks: our Card Alignment Game, designed to isolate brittle convention formation, and the more challenging Level-Based Foraging and OvercookedV2 benchmarks. Our experiments consistently show that a joint-attention-inspired signal improves coordination with unknown partners, underlining MATE's potential as a general coordination mechanism that complements and surpasses symmetry-breaking approaches.
