Can LLMs learn emergent patterns in multi-agent RL?
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments
October 4, 2024
https://arxiv.org/pdf/2410.02516- This research introduces BUN (Bottom-up Network), a novel approach for training sparse neural networks in multi-agent reinforcement learning (MARL).
- BUN treats a multi-agent system as a single agent with a sparse neural network, promoting independent agent actions and requiring less communication. This is particularly relevant for LLM-based agents where communication can be computationally expensive.
- The network connections in BUN emerge dynamically during training based on gradient information, allowing for flexible coordination when needed. This emergent communication aligns with the potential of LLMs to dynamically adapt their communication strategies based on the task.