Metric-Gradient Projection for Stable Multi-Agent Policy Learning
The paper introduces a new approach called Hodge-Projected Multi-agent Learning (HPML) aimed at improving stability in multi-agent reinforcement learning (MARL). HPML addresses the challenges posed by the coupling of agents' policy updates that can lead to slow or unstable learning. The method utilizes a metric-gradient projection to enhance the optimization landscape, demonstrating improved stability and performance in controlled experiments.
- ▪HPML projects the joint update field of a multi-agent system onto a metric-gradient component.
- ▪The approach is characterized by a Poisson-type equation and implemented through graph-based and neural realizations.
- ▪Controlled experiments validate the effectiveness of HPML in enhancing stability and normalized return in MARL pipelines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18809 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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Computer Science > Machine Learning arXiv:2605.18809 (cs) [Submitted on 12 May 2026] Title:Metric-Gradient Projection for Stable Multi-Agent Policy Learning Authors:Zuyuan Zhang, Sizhe Tang, Mahdi Imani, Tian Lan View a PDF of the paper titled Metric-Gradient Projection for Stable Multi-Agent Policy Learning, by Zuyuan Zhang and 2 other authors View PDF HTML (experimental) Abstract:General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape faced by the others. This coupling can entangle an integrable component of collective improvement with cyclic interaction dynamics, leading to slow or unstable multi-agent learning.
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