How can I optimize communication in multi-agent RL?
Robust Event-Triggered Integrated Communication and Control with Graph Information Bottleneck Optimization
This research tackles the problem of efficient communication in multi-agent reinforcement learning (MARL) where agents have limited information. It introduces the Consensus-Driven Event-based Graph Information Bottleneck (CDE-GIB) method. CDE-GIB reduces communication overhead by triggering information exchange only when important and compressing messages using a Graph Information Bottleneck (GIB) that considers both data content and the communication network structure. It utilizes a variable threshold for triggering communication updates and optimizes the bottleneck considering the desired consensus state and global observation.
For LLM-based multi-agent systems, CDE-GIB offers a potential solution to managing the communication bandwidth and computational costs associated with exchanging large language model outputs. The variable threshold event triggering helps filter less important information, and GIB enables concise message representation, both critical for efficient interactions among LLM agents. The focus on consensus establishment relates directly to the need for shared understanding and coherent action among agents in collaborative tasks.