G2MAF: Test-Time Gradient Guidance for Multi-Agent Flow Policies
Offline multi-agent reinforcement learning (MARL) learns cooperative policies from fixed datasets without further environment interaction and a learned policy is frozen at deployment. Such a frozen policy typically proposes a single joint action and executes it directly at deployment time. However, this one-shot deployment often commits to a suboptimal proposal, even when better nearby…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
