How can agents learn to cooperate better with limited information?
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning
This paper introduces SICA (Selective Implicit Collaboration Algorithm), a novel framework for cooperative multi-agent reinforcement learning aimed at improving performance in communication-restricted settings. SICA enables agents to selectively filter relevant information based on environment dynamics and learn tacit cooperation with other agents without explicit communication during execution, similar to how humans collaborate effectively in teams.
Key points for LLM-based multi-agent systems include the adaptive information selection mechanism, enabling LLMs to focus on crucial information for decision-making, and the shift from explicit to implicit communication through a regeneration block, allowing LLMs to act independently based on learned cooperative behaviors, potentially streamlining coordination and reducing communication overhead.