Summary of A First Introduction to Cooperative Multi-agent Reinforcement Learning, by Christopher Amato
A First Introduction to Cooperative Multi-Agent Reinforcement Learning
by Christopher Amato
First submitted to arxiv on: 10 May 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 presents a comprehensive overview of multi-agent reinforcement learning (MARL) approaches, which have gained significant attention in recent years. The study categorizes MARL methods into three main types: centralized training and execution (CTE), centralized training for decentralized execution (CTDE), and decentralized training and execution (DTE). CTE methods assume centralization during both training and execution, while CTDE methods leverage centralized information during training but enable decentralized execution. DTE methods make the fewest assumptions and are often simple to implement. The paper explores these approaches, highlighting their strengths and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research on multi-agent reinforcement learning (MARL) has seen a surge in recent years. The study groups MARL methods into three main categories: CTE, CTDE, and DTE. CTE methods work well when all agents can share information freely. CTDE methods are common because they allow for decentralized execution. DTE methods make the fewest assumptions and are easy to understand. |
Keywords
* Artificial intelligence * Attention * Reinforcement learning
