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 alternatives remain consistent with the behavior data. To address this issue, we propose Gradient Guided Multi Agent Flow (G2MAF), a refinement framework for optimizing joint policies at test-time. G2MAF applies one globally normalized, projected critic gradient to guide and coordinate all agents' correctio...
| Authors | Guowei Zou, Haitao Wang, Guoxin Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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