How can humans help AI agents work together better?
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement Learning
September 19, 2024
https://arxiv.org/pdf/2409.11741This paper presents HARP, a new system for improving multi-agent teamwork in AI. HARP lets non-expert humans give feedback on how agents are grouped, leading to better strategies than AI alone. This is especially useful in complex tasks like StarCraft II, where HARP achieved a 100% win rate.
Key to HARP's success are: 1) dynamic regrouping: agents can be rearranged based on human feedback, making the system adaptable, and 2) a "permutation invariant" approach: HARP understands the roles of agents within a group, no matter their order, leading to more robust strategy suggestions. This is promising for future work where LLMs could give feedback based on analyzing different data sources.