Summary of Communication Learning in Multi-agent Systems From Graph Modeling Perspective, by Shengchao Hu et al.
Communication Learning in Multi-Agent Systems from Graph Modeling Perspective
by Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this study, researchers aim to enhance collaboration among multiple intelligent agents by developing a novel approach for distributed communication. The existing frameworks can be resource-intensive and limiting, as they often remain static during inference. To address these issues, the authors introduce CommFormer, a bi-level optimization process that learns the communication graph while updating architectural parameters. This approach utilizes continuous relaxation of the graph representation and attention units to efficiently optimize the communication graph. Additionally, the authors propose a temporal gating mechanism for each agent, enabling dynamic decisions on information sharing based on current observations. Extensive experiments demonstrate the robustness of CommFormer across various cooperative scenarios, allowing agents to develop more coordinated strategies regardless of changes in the number of agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores ways to improve communication between multiple intelligent agents working together. Currently, frameworks for distributed communication can be inefficient and limiting because they don’t adapt well during decision-making. The researchers introduce a new approach called CommFormer that learns how agents should communicate with each other while also adjusting the way information is processed. This helps agents make better decisions by deciding what information to share and when. |
Keywords
* Artificial intelligence * Attention * Inference * Optimization
