Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

Understand

With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning.

• Although pursuing novelty, diversity, or uncertainty attracts increasing attention, redundant efforts brought by exploration without proper guidance choices poses a practical issue for the community.
• This paper introduces a systematic approach, termed LEMAE, choosing to channel informative task-relevant guidance from a knowledgeable Large Language Model (LLM) for Efficient Multi-Agent Exploration.
• Specifically, we ground linguistic knowledge from LLM into symbolic key states, that are critical for task fulfillment, in a discriminative manner at low LLM inference costs.