Summary of Group-aware Coordination Graph For Multi-agent Reinforcement Learning, by Wei Duan et al.
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
by Wei Duan, Jie Lu, Junyu Xuan
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to Cooperative Multi-Agent Reinforcement Learning (MARL) by inferring a Group-Aware Coordination Graph (GACG). The GACG captures both cooperation between agent pairs and group-level dependencies from behavior patterns. To ensure behavioral consistency, the authors introduce a group distance loss that promotes group cohesion and specialization. Evaluations on StarCraft II micromanagement tasks demonstrate the superiority of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers work together better by figuring out how agents interact with each other. It creates a special graph that shows how groups of agents behave similarly, which makes it easier for them to make decisions. The researchers tested their idea on a game and showed that it works really well. |
Keywords
» Artificial intelligence » Reinforcement learning
