MA-WAM: Multi-Agent World-Action Model for Test-Time Planning
Multi-agent cooperative tasks require different agents to execute a joint action simultaneously, and each agent's action affects both the observations and responses of the other agents. Hence, a world model is needed to predict the team return resulting from the joint actions of all agents. A naive extension directly applies a single-agent world model to each agent's action when predicting the…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
