MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria
Summary
The paper introduces MEMOA, a decentralized strategy for massive online agents that achieves optimality via mean-field Nash equilibria, outperforming greedy baselines while scaling better than centralized approaches.
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# MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria Source: [https://arxiv.org/abs/2605.05492](https://arxiv.org/abs/2605.05492) [View PDF](https://arxiv.org/pdf/2605.05492) > Abstract:In the modern age of large\-scale AI, federated learning has become an increasingly important tool for training large populations of AI agents; however, its computational and communication costs can rapidly fail to scale with the number of agents\. This is precisely where decentralized agentic strategies shine: each agent acts autonomously, using only its own state together with a minimal summary of the ensemble, namely the mean\-field\. We derive the unique optimal decentralized policy in closed form\. Optimality is characterized through a worst\-client/minimax criterion: minimizing the under\-performer regret, namely the maximal online cost incurred by the weakest agent in the ensemble\. We further prove that the resulting decentralized policy asymptotically converges, in the large\-population limit, to the Nash\-optimal centralized policy, whose direct computation is not scalable\. We use an online weighting mechanism to optimize the server\-computed mixture of client predictions, thereby improving the mean prediction in addition to the previously optimized weakest\-client prediction\. Numerical experiments verify our theoretical guarantees and demonstrate that our decentralized policy typically outperforms natural greedy decentralized baselines\. ## Submission history From: Xuwei Yang \[[view email](https://arxiv.org/show-email/90454a93/2605.05492)\] **\[v1\]**Wed, 6 May 2026 22:26:59 UTC \(200 KB\)
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