Dynamic Communication in Multi-Agent Reinforcement Learning via Information Bottleneck

Effective information sharing is essential for multi-agent systems to execute cooperative tasks successfully. Typically, agents within such systems are either stationary or possess unrestricted communication ranges. However, in more complex scenarios where agent mobility is introduced, the communication network’s topology becomes dynamic over time. This dynamism can result in partial communication unreachability among certain agents. Consequently, striking a balance between minimizing overall communication overhead and optimizing task performance becomes a formidable challenge. In this paper, we address the issue of dynamic communication in multi-agent systems. We propose a novel approach that leverages the principle of information bottleneck theory to develop a multi-mean field multi-agent reinforcement learning algorithm called MMIB. Through a series of experiments, we demonstrate the effectiveness of our proposed algorithm in reducing communication overhead while maintaining task performance at a level comparable to other state-of-the-art multi-agent reinforcement learning algorithms.