Summary of Multi-agent Reinforcement Learning with Selective State-space Models, by Jemma Daniel et al.
Multi-Agent Reinforcement Learning with Selective State-Space Models
by Jemma Daniel, Ruan de Kock, Louay Ben Nessir, Sasha Abramowitz, Omayma Mahjoub, Wiem Khlifi, Claude Formanek, Arnu Pretorius
First submitted to arxiv on: 25 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 The paper explores the application of State-Space Models (SSMs) in Multi-Agent Reinforcement Learning (MARL), specifically the Mamba algorithm, to address the scalability limitations of Transformer models. The authors introduce a modified version of the Multi-Agent Transformer (MAT) that incorporates standard and bi-directional Mamba blocks, as well as a novel “cross-attention” Mamba block, dubbed Multi-Agent Mamba (MAM). Experimental results show that MAM matches MAT’s performance across multiple MARL environments while offering superior scalability to larger agent scenarios. This breakthrough has significant implications for the MARL community, suggesting that SSMs could replace Transformers without compromising performance and support more effective scaling to higher numbers of agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve how computers work with many robots is being explored. Right now, a popular method called Transformer doesn’t work well when there are too many robots because it uses too much computer power. A different approach called State-Space Model (SSM) might be able to do the job better. The authors of this paper took an existing robot-training algorithm and modified it to use SSMs, making it faster and more efficient. They tested their new version, called Multi-Agent Mamba, on many scenarios and found that it worked just as well as the old Transformer method but used much less computer power. This is exciting news for people working with robots because it means they might be able to control even more robots in the future. |
Keywords
» Artificial intelligence » Cross attention » Reinforcement learning » Transformer
