How can LLMs help agents cooperate better?
Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning
October 22, 2024
https://arxiv.org/pdf/2410.15841This paper proposes a novel method called Factor-based Multi-Agent Transformer (f-MAT) for efficient collaboration in multi-agent reinforcement learning systems.
For LLM-based multi-agent systems, f-MAT offers:
- Efficient communication: Utilizes "factors" (groups of agents) and a transformer architecture to enable efficient message passing between agents, particularly beneficial for large-scale systems.
- Decentralized execution with centralized training: Allows agents to make decisions based on local observations during execution, while benefiting from centralized training for better coordination.
- Parallel action generation: Generates actions in parallel instead of sequentially, speeding up decision-making in time-sensitive applications.
- Adaptability to diverse environments: Handles both homogeneous and heterogeneous agent settings with varying communication needs.