How can I improve MARL agent communication efficiency?
Revisiting Communication Efficiency in Multi-Agent Reinforcement Learning from the Dimensional Analysis Perspective
This paper explores how to make communication between multiple AI agents more efficient, particularly in complex scenarios. It argues that simply filtering messages isn't enough; agents also need to process received information effectively. The proposed method, DRMAC, uses a two-pronged approach: reducing redundancy in combined messages to avoid repetition and training a network to identify and prioritize the most important parts of a message for each agent's individual needs. This is particularly relevant to LLM-based multi-agent systems, where LLMs can generate large amounts of potentially redundant text, and efficient information processing is key for coordination. The dimensional analysis approach and the focus on the receiving agent's processing offer valuable insights for building efficient communication channels between LLMs acting as agents.