ZhuohuiZhang/TGCNet
This is the official implementation of [AAAI'25 Oral] accepted paper: Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems.
This helps AI researchers and developers working on multi-agent systems to improve how their agents coordinate and communicate. It takes existing multi-agent training environments and algorithms as input and produces agents that learn more efficient and robust communication strategies. Researchers in AI and machine learning focusing on cooperative AI behaviors would use this.
No commits in the last 6 months.
Use this if you are an AI researcher or developer working on cooperative multi-agent reinforcement learning and want to develop agents that can dynamically form communication networks for better collaboration.
Not ideal if you are a practitioner looking for a ready-to-use solution for a specific real-world problem, rather than a research tool for multi-agent system development.
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Language
Python
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Last pushed
Feb 11, 2025
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