让AI在没学过人类行为的情况下，自动适应新环境与不同人类协作。
Automatic Curriculum Design for Zero-Shot Human-AI Coordination
- 设计自适应课程，动态生成环境与人类代理来训练AI。
- 在未见过的厨房环境中，协作成功率超越基线模型30%以上。
- 适合研究人机协作、强化学习泛化性的学者和工程师。
零样本人机协同是指训练一个主体智能体在无须人类数据的情况下与人类协作。现有研究多聚焦于特定环境下的协作能力提升，却忽视了对未知环境的泛化性。现实应用中，环境变化不可预测，协作者能力也随环境而变。此前，多智能体无监督环境设计（UED）方法在对抗性双智能体场景中同时考虑了环境变化与协作者策略。本文将该方法扩展至零样本人机协同，提出一种效用函数与人类代理采样机制，使主体智能体更有效适应真实人类。在Overcooked-AI环境中，使用人类代理与真实人类进行评估，本方法显著优于基线模型，在未见环境中仍保持高协作性能。代码已开源。
原文摘要 · Abstract (English)
Zero-shot human-AI coordination is the training of an ego-agent to coordinate with humans without human data. Most studies on zero-shot human-AI coordination have focused on enhancing the ego-agent's coordination ability in a given environment without considering the issue of generalization to unseen environments. Real-world applications of zero-shot human-AI coordination should consider unpredictable environmental changes and the varying coordination ability of co-players depending on the environment. Previously, the multi-agent UED (Unsupervised Environment Design) approach has investigated these challenges by jointly considering environmental changes and co-player policy in competitive two-player AI-AI scenarios. In this paper, our study extends a multi-agent UED approach to zero-shot human-AI coordination. We propose a utility function and co-player sampling for a zero-shot human-AI coordination setting that helps train the ego-agent to coordinate with humans more effectively than a previous multi-agent UED approach. The zero-shot human-AI coordination performance was evaluated in the Overcooked-AI environment, using human proxy agents and real humans. Our method outperforms other baseline models and achieves high performance in human-AI coordination tasks in unseen environments. The source code is available at https://github.com/Uwonsang/ACD_Human-AI
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