Improving Human-AI Coordination through Adversarial Training and Generative Models

Being able to cooperate with new people is an important component of many economically valuable AI tasks, from household robotics to autonomous driving. However, generalizing to novel humans requires training on data that captures the diversity of human behaviors. Adversarial training is one avenue for searching for such data and ensuring that agents are robust. However, it is difficult to apply in the cooperative setting because adversarial policies intentionally learn to sabotage the task instead of simulating valid cooperation partners. To address this challenge, we propose a novel strategy for overcoming self-sabotage that combines a pre-trained generative model to simulate valid cooperative agent policies with adversarial training to maximize regret. We call our method GOAT: Generative Online Adversarial Training. In this framework, the GOAT dynamically searches for and generates coordination strategies where the learning policy -- the Cooperator agent -- underperforms. GOAT enables better generalization by exposing the Cooperator to various challenging interaction scenarios. We maintain realistic coordination strategies by updating only the generative model's embedding while keeping its parameters frozen, thus avoiding adversarial exploitation. We evaluate GOAT with real human partners, and the results demonstrate state-of-the-art performance on the Overcooked benchmark, highlighting its effectiveness in generalizing to diverse human behaviors.