Summary of Sable: a Performant, Efficient and Scalable Sequence Model For Marl, by Omayma Mahjoub et al.
Sable: a Performant, Efficient and Scalable Sequence Model for MARL
by Omayma Mahjoub, Sasha Abramowitz, Ruan de Kock, Wiem Khlifi, Simon du Toit, Jemma Daniel, Louay Ben Nessir, Louise Beyers, Claude Formanek, Liam Clark, Arnu Pretorius
First submitted to arxiv on: 2 Oct 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 As multi-agent reinforcement learning (MARL) tackles increasingly complex problems, it’s crucial for algorithms to excel in three key areas: strong performance, memory efficiency, and scalability. This paper introduces Sable, a sequence modeling approach that achieves these properties. Building upon Retentive Networks (Sun et al., 2023), Sable efficiently processes multi-agent observations with long context memory for temporal reasoning. Extensive evaluations across six diverse environments demonstrate Sable’s significant performance gains in 34 out of 45 tested tasks, while maintaining scalability and efficient memory usage even with over a thousand agents. Ablation studies confirm the source of Sable’s performance boosts and highlight its computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine robots working together to solve complex problems. To do this, they need to learn from each other and remember what they’ve seen before. This paper introduces an algorithm called Sable that helps these robots work better together. Sable is good at learning and remembering, and it can handle a lot of information at once. The researchers tested Sable in many different scenarios and found that it did better than other algorithms in most cases. They also showed that Sable can handle even more complex situations as the number of robots increases. This means that Sable could be used to create smarter robots that can work together to solve big problems. |
Keywords
* Artificial intelligence * Reinforcement learning
