通过状态阻断提升零样本协作能力，让智能体更适应未见过的合作者。
Shaping Zero-Shot Coordination via State Blocking
- 用状态阻断生成虚拟环境，模拟多样合作场景。
- 在多个基准上实现更强的零样本协作表现，包括对人类合作者的泛化。
- 无需修改环境即可提升协作鲁棒性，适合真实多智能体系统。
零样本协作（ZSC）旨在使独立训练的智能体在无先前交互的情况下与伙伴协作，这是现实世界多智能体系统和人机协作的关键需求。现有方法主要强调训练中增加伙伴多样性，但此类策略往往难以可靠地泛化到未见伙伴。我们提出状态阻断协作（SBC），一种简单而有效的方法，通过状态阻断生成一系列虚拟环境，使智能体在不直接修改环境的前提下体验多种次优伙伴策略。在多个基准测试中，SBC展现出卓越的零样本协作性能，包括对人类伙伴的强大泛化能力。
原文摘要 · Abstract (English)
Zero-shot coordination (ZSC) aims to enable agents to cooperate with independently trained partners without prior interaction, a key requirement for real-world multi-agent systems and human-AI collaboration. Existing approaches have largely emphasized increasing partner diversity during training, yet such strategies often fall short of achieving reliable generalization to unseen partners. We introduce State-Blocked Coordination (SBC), a simple yet effective framework that improves ZSC by inducing diverse interaction scenarios without direct environment modification. Specifically, SBC generates a family of virtual environments through state blocking, allowing agents to experience a wide range of suboptimal partner policies. Across multiple benchmarks, SBC demonstrates superior performance in zero-shot coordination, including strong generalization to human partners.
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