SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning

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Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning
2025Xinran Li, Xiaolu Wang et al.
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2024Shengchao Hu, Li Shen et al.
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Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
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Founder's Pitch

"SCoUT enhances multi-agent MARL communication with scalable, utility-driven temporal grouping, delivering precise credit assignment and decentralized execution capabilities."

Reinforcement LearningScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

3/4 signals

7.5

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