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

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References (42)

[1]

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning

2025Xinran Li, Xiaolu Wang et al.

[2]

Learning Multi-Agent Communication from Graph Modeling Perspective

2024Shengchao Hu, Li Shen et al.

[3]

Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning

Founder's Pitch

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

Reinforcement Learning•Score: 8•View PDF ↗

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

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References (42)

Founder's Pitch

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

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

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References (42)

Founder's Pitch

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

Commercial Viability Breakdown

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