Imported from hiyenwong/ai_collection (collection/skills/security-privacy/aggregate-in-the-advantage-not-the-ratio-a-canonic/SKILL.md). Install upstream with npx skills add hiyenwong/ai_collection --skill aggregate-in-the-advantage-not-the-ratio-a-canonic. Copyright stays with the author.

Aggregate in the Advantage, Not the Ratio: A Canonical-Form Analysis of Cooperative Multi-Agent Policy Optimization

Name: aggregate-in-the-advantage-not-the-ratio-a-canonic
Author: hiyenwong

Derived from arXiv:2607.17924 - Aggregate in the Advantage, Not the Ratio: A Canonical-Form Analysis of Cooperative Multi-Agent Policy Optimization

Core Concept

Multi-agent policy optimization, exemplified by PPO-based methods, is a key branch of cooperative Multi-Agent Reinforcement Learning (MARL). A central design question is how many neighboring agents\footnote{In this paper, "neighbors" refer not only to physical proximity but also to agents whose actions influence one another.} to aggregate in order to effectively utilize global information for cooperation. This decision must be made along two dimensions: in the advantage (which agents' rewards co...

Key Insights

Derived from arXiv:2607.17924
Published: 2026-07-20
Utility Score: 1.00
Authors: Zijian Zhao, Sen Li

Activation

aggregate-in-the-advantage-not-the-ratio-a-canonic, 2607.17924

References

arXiv: https://arxiv.org/abs/2607.17924

# after Install: the listing is in your workspace's routing pool - a plan picks it for its slot curl -s -X POST https://api.opensmartroute.ai/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'

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aggregate-in-the-advantage-not-the-ratio-a-canonic

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Aggregate in the Advantage, Not the Ratio: A Canonical-Form Analysis of Cooperative Multi-Agent Policy Optimization

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