aiqbench analysis
This research addresses the fundamental scalability bottleneck in cooperative multi-agent reinforcement learning by introducing a descent-guided policy gradient method that leverages analytical system models to filter out cross-agent noise. By reducing estimator variance from O(N) to O(1), the proposed framework enables stable convergence even in large-scale heterogeneous environments like cloud scheduling and power dispatch with up to 1,500 agents. The work represents a significant theoretical and practical advance for deploying scalable AI solutions in complex engineering systems where traditional shared-reward approaches fail.
Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning
arXiv:2602.20078v4 Announce Type: replace-cross Abstract: Scaling cooperative multi-agent reinforcement learning (MARL) is fundamentally limited by cross-agent noise. When agents share a common reward, each agent's learning signal is computed from a shared return that depends on all agents, so the stochasticity of the other agents enters the signal as cross-agent noise that grows with $N$. Many engineering systems, such as cloud computing and power systems, have differentiable analytical models that prescribe efficient system states, providing a new reference beyond noisy shared returns. In this work, we propose Descent-Guided Policy Gradient (DG-PG), a framework that augments policy-gradient updates with a low-noise descent signal toward this reference. We prove that DG-PG reduces policy-gradient estimator variance from $\mathcal{O}(N)$ to $\mathcal{O}(1)$, preserves the stationary points of the cooperative objective, and achieves agent-independent sample complexity $\mathcal{O}(1/\epsilon^3)$. We evaluate DG-PG on cloud resource scheduling and power dispatch tasks, which involve discrete and continuous decisions, respectively. In both tasks, DG-PG converges within 10 episodes on average at every scale up to 1500 heterogeneous agents.
Abstract:Scaling cooperative multi-agent reinforcement learning (MARL) is fundamentally limited by cross-agent noise. When agents share a common reward, each agent's learning signal is computed from a shared return that depends on all agents, so the stochasticity of the other agents enters the signal as cross-agent noise that grows with $N$. Many engineering systems, such as cloud computing and power systems, have differentiable analytical models that prescribe efficient system states, providing a new reference beyond noisy shared returns. In this work, we propose Descent-Guided Policy Gradient (DG-PG), a framework that augments policy-gradient updates with a low-noise descent signal toward this reference. We prove that DG-PG reduces policy-gradient estimator variance from $\mathcal{O}(N)$ to $\mathcal{O}(1)$, preserves the stationary points of the cooperative objective, and achieves agent-independent sample complexity $\mathcal{O}(1/\epsilon^3)$. We evaluate DG-PG on cloud resource scheduling and power dispatch tasks, which involve discrete and continuous decisions, respectively. In both tasks, DG-PG converges within 10 episodes on average at every scale up to 1500 heterogeneous agents.
Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2602.20078 [cs.MA]
(or arXiv:2602.20078v4 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2602.20078 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 260 words
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