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<p>Developing intelligent agents that can effectively coordinate with diverse human partners is a fundamental goal of artificial general intelligence. Previous approaches typically generate a variety of partners to cover human policies, and then either train a single universal agent or maintain multiple best-response (BR) policies for different partners. However, the first direction struggles with the stochastic and multimodal nature of human behaviors, and the second relies on costly few-shot adaptations during policy deployment, which is unbearable in real-world applications such as healthcare and autonomous driving. Recognizing that human partners can easily articulate their preferences or behavioral styles through natural languages (NLs) and make conventions beforehand, we propose a framework for Human-AI Coordination via Policy Generation from Language-guided Diffusion (Haland). Haland first trains BR policies for various partners using reinforcement learning, and then compresses policy parameters into a single latent diffusion model, conditioned on task-relevant language derived from their behaviors. Finally, the alignment between task-relevant and NLs is achieved to facilitate efficient human-AI coordination. Empirical evaluations across diverse cooperative environments demonstrate that Haland generates agents with significantly enhanced zero-shot coordination performance, utilizing only NL instructions from various partners, and outperforms existing methods by approximately 89.64%.</p>

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Human-AI coordination via policy generation from language-guided diffusion

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Kunmin Lin,
  • Lei Yuan,
  • Ziqian Zhang,
  • Lihe Li,
  • Feng Chen,
  • Yang Yu

摘要

Developing intelligent agents that can effectively coordinate with diverse human partners is a fundamental goal of artificial general intelligence. Previous approaches typically generate a variety of partners to cover human policies, and then either train a single universal agent or maintain multiple best-response (BR) policies for different partners. However, the first direction struggles with the stochastic and multimodal nature of human behaviors, and the second relies on costly few-shot adaptations during policy deployment, which is unbearable in real-world applications such as healthcare and autonomous driving. Recognizing that human partners can easily articulate their preferences or behavioral styles through natural languages (NLs) and make conventions beforehand, we propose a framework for Human-AI Coordination via Policy Generation from Language-guided Diffusion (Haland). Haland first trains BR policies for various partners using reinforcement learning, and then compresses policy parameters into a single latent diffusion model, conditioned on task-relevant language derived from their behaviors. Finally, the alignment between task-relevant and NLs is achieved to facilitate efficient human-AI coordination. Empirical evaluations across diverse cooperative environments demonstrate that Haland generates agents with significantly enhanced zero-shot coordination performance, utilizing only NL instructions from various partners, and outperforms existing methods by approximately 89.64%.