让AI学会根据搭档特点灵活调整动作，提升人机协作效率。
Adaptive Human-AI Coordination via Hierarchical Action Disentanglement
- 分层强化学习框架，用高层技能控制底层动作序列
- 在多种布局和未见过的伙伴下，表现优于现有方法
- 适合需要动态适应的实时人机协作场景
人机协作需要能适应不同合作方行为和能力水平的智能体，同时对未知合作者保持鲁棒性。现有方法常退化为单一主导行为或学习到不匹配的技能，限制了有效协作。我们提出内在动作解耦（IAD），一种深度分层强化学习（DHRL）框架，学习与高层潜在技能相关的、面向合作方的低层动作序列。IAD引入内在奖励，显式鼓励低层策略在不同技能间产生解耦的动作分布，实现高层决策与特定合作方行为响应之间的可解释映射。通过捕捉时间上延展的交互模式，IAD在分布外变化下仍能灵活适应异构合作动态。我们在Overcooked-AI环境中评估IAD，覆盖多个布局及多样化合作设置，包括未见过的模拟合作者、基于人类-人类游戏数据训练的人类代理模型以及真实人类合作者。结果表明，IAD在所有设置中均持续优于强基线，展现出更可靠、自适应的协作能力。
原文摘要 · Abstract (English)
Human-AI collaboration requires agents that can adapt to diverse partner behaviors and skill levels while remaining robust to unseen partners. Existing methods often collapse to a single dominant behavior or learn poorly aligned skills, limiting effective coordination. We propose Intrinsic Action Disentanglement (IAD), a deep hierarchical reinforcement learning (DHRL) framework that learns distinct, partner-aware low-level action sequences conditioned on high-level latent skills. IAD introduces an intrinsic reward that explicitly encourages disentangled action distributions of the agent's low-level policy across skills, yielding an interpretable mapping between high-level decisions and partner-specific behavioral responses. By capturing temporally extended interaction patterns, IAD enables flexible adaptation to heterogeneous partner dynamics under distributional shift. We evaluate IAD in the Overcooked-AI domain across multiple layouts and diverse partner settings, including unseen simulated partners, a human-proxy model trained on human-human gameplay, and real human partners. Results show that IAD consistently outperforms strong baselines and achieves more reliable, adaptive coordination across all settings.
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