Summary of Self-clustering Hierarchical Multi-agent Reinforcement Learning with Extensible Cooperation Graph, by Qingxu Fu et al.
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
by Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Xiaolin Ai
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposes Hierarchical Cooperation Graph Learning (HCGL), a novel Multi-Agent Reinforcement Learning (MARL) model for solving complex cooperative challenges. HCGL consists of three components: an Extensible Cooperation Graph (ECG) that achieves self-clustering cooperation, graph operators that adjust ECG’s topology, and an MARL optimizer that trains these operators. Unlike other MARL models, HCGL guides agent behaviors through ECG’s topology rather than policy neural networks. The paper demonstrates HCGL’s effectiveness in multi-agent benchmarks with sparse rewards and high zero-shot transfer success rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for robots to work together by using a special kind of computer program called Hierarchical Cooperation Graph Learning (HCGL). This program is like a map that helps the robots figure out how to work together more effectively. The map has three parts: one part shows which robots are working together, another part changes how the robots connect with each other, and the third part makes sure all the robots are working towards the same goal. This new way of doing things is important because it allows the robots to learn from each other and work better together, even in situations where they can’t see what’s happening around them. |
Keywords
» Artificial intelligence » Clustering » Reinforcement learning » Zero shot
