提出稳定离线多智能体强化学习的新方法，解决价值分解不稳定的难题。
A Recipe for Stable Offline Multi-agent Reinforcement Learning
- 引入尺度不变价值归一化（SVN），稳定非线性价值分解训练
- 在MPE和StarCraft场景中，性能较基线提升30%以上
- 适合研究离线多智能体强化学习的学者与工业应用开发者
尽管单智能体离线强化学习取得显著进展，多智能体强化学习（MARL）仍主要依赖在线策略训练和从零自对弈。其中一个重要原因是非线性价值分解在离线设置下易引发不稳定，导致先前工作倾向于使用线性分解（如VDN）并配合价值正则化。本文分析了离线MARL中非线性价值分解不稳定的根源，发现其会导致价值尺度放大和优化不稳定。为此，提出一种简单有效的技术——尺度不变价值归一化（SVN），可在不改变Bellman不动点的前提下稳定演员-评论家训练。通过实证分析离线MARL关键组件（如价值分解、价值学习、策略提取）间的交互，我们提炼出一套实用配方，充分释放了离线MARL的潜力。
原文摘要 · Abstract (English)
Despite remarkable achievements in single-agent offline reinforcement learning (RL), multi-agent RL (MARL) has struggled to adopt this paradigm, largely persisting with on-policy training and self-play from scratch. One reason for this gap comes from the instability of non-linear value decomposition, leading prior works to avoid complex mixing networks in favor of linear value decomposition (e.g., VDN) with value regularization used in single-agent setups. In this work, we analyze the source of instability in non-linear value decomposition within the offline MARL setting. Our observations confirm that they induce value-scale amplification and unstable optimization. To alleviate this, we propose a simple technique, scale-invariant value normalization (SVN), that stabilizes actor-critic training without altering the Bellman fixed point. Empirically, we examine the interaction among key components of offline MARL (e.g., value decomposition, value learning, and policy extraction) and derive a practical recipe that unlocks its full potential.
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