A Recipe for Stable Offline Multi-agent Reinforcement Learning
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 p...
| Authors | Dongsu Lee, Daehee Lee, Amy Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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