Summary of Fully Independent Communication in Multi-agent Reinforcement Learning, by Rafael Pina et al.
Fully Independent Communication in Multi-Agent Reinforcement Learning
by Rafael Pina, Varuna De Silva, Corentin Artaud, Xiaolan Liu
First submitted to arxiv on: 26 Jan 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 The paper explores Multi-Agent Reinforcement Learning (MARL) and its communication approaches. While previous research has proposed various methods, they might still be too complex for practical applications. This study investigates how independent learners without shared parameters can communicate and proposes a new learning scheme to overcome challenges. Results show that agents can learn effective communication strategies despite these difficulties. Additionally, the paper examines how network capacities affect MARL communication with both parameter sharing and not sharing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines learn together in different situations. When they don’t share information, it’s harder for them to work together effectively. The researchers found a new way for agents to communicate without shared parameters. They tested this method and saw that agents can still learn good ways of working together despite the challenges. They also studied how different network sizes affect communication between machines. |
Keywords
* Artificial intelligence * Reinforcement learning
