How can I asynchronously train human-AI teams in complex games?
Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment
This paper explores asynchronous training for multi-agent teams, where trainees practice with AI teammates instead of humans to alleviate scheduling constraints. It introduces Overcooked-AI: Have You Been Served? (HYBS), a partially observable game with distinct roles, as a testbed. Key points for LLM-based multi-agent systems include: using unsupervised clustering of behavior trajectories to categorize training partners, combining imitation learning and heuristic approaches for AI teammate design, and focusing evaluation on generalization to unseen teammate strategies. While initial results show no significant improvement in evaluation performance due to the asynchronous training, the paper offers valuable design recommendations for future research, such as better integration between training and evaluation setups and developing more human-like AI teammate behaviors to enhance training experience and outcomes.