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

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%.