Can offline data train AI agents for large-scale games?
Scalable Offline Reinforcement Learning for Mean Field Games
October 24, 2024
https://arxiv.org/pdf/2410.17898This paper introduces Off-MMD, an algorithm for training AI agents in a multi-agent setting using pre-existing, static data, without requiring real-time interaction.
Off-MMD is particularly relevant to LLM-based multi-agent systems because:
- Offline Training: It enables training LLM agents on large datasets of text interactions, like dialogue logs, without requiring agents to learn by interacting in a live environment.
- Scalability: Off-MMD uses the 'mean-field' concept, approximating the influence of a large population of agents, making it potentially scalable to systems with many LLM agents.
- Equilibrium Finding: It aims to find policies where individual agents are strategically aligned with the overall population, crucial for stable and coherent behavior in multi-agent LLM systems.