How can agents learn to communicate effectively in multi-agent systems?
Communication Learning in Multi-Agent Systems from Graph Modeling Perspective
This paper introduces CommFormer, a new method for improving communication efficiency in multi-agent reinforcement learning (MARL) systems, particularly relevant for resource-constrained environments. It learns an optimal communication graph between agents, deciding who should communicate with whom, and dynamically controls when communication happens based on current observations. This reduces unnecessary information exchange.
For LLM-based multi-agent systems, CommFormer offers a way to manage communication between multiple LLMs, optimizing both the content and timing of message exchanges, making collaboration more efficient and potentially improving overall performance and scalability by reducing the communication overhead typically associated with fully connected agent communication graphs. It also leverages attention mechanisms to process messages within the defined communication graph dynamically. CommFormer allows individual LLMs to dynamically decide when they need to incorporate information from other agents based on their current state, promoting more efficient collaboration.