Can Mamba-based agents outperform MAT with fewer resources?
Multi-Agent Reinforcement Learning with Selective State-Space Models
October 28, 2024
https://arxiv.org/pdf/2410.19382-
Replacing attention mechanisms in Multi-Agent Reinforcement Learning (MARL) with a faster, more scalable method called Mamba. This allows for handling more agents without sacrificing performance.
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Mamba's recurrent nature and linear scaling make it more efficient than attention-based models for real-time decision-making in multi-agent systems, especially with large numbers of agents. This efficiency makes Mamba particularly relevant for developing complex LLM-based multi-agent applications.