ReSCOM: Reward-Shaped Curriculum for Efficient Multi-Agent Communication Learning

Communication enhances collaboration among artificial intelligence agents. Given the conflicts between limited communication resources and communication needs, learning effective communication strategies is essential. We observe that incorporating learning to communicate can complicate mastering primary tasks. This is due to the uncertainty in information acquisition during the learning process, which can lead to an unstable environment for primary tasks. In this paper, we introduce ReSCOM, an efficient joint learning framework that combines learning-to-communicate with primary tasks. ReSCOM progressively adjusts the learning emphasis through reward-shaped curriculums, allowing agents to shift their focus from primary tasks and basic communication tasks (e.g., how to encode) to advanced communication strategies (e.g., determining when it is worthwhile to communicate). This approach minimizes the impact on the learning efficiency of primary tasks while simultaneously facilitating communication learning. We evaluate ReSCOM against state-of-the-art methods across various tasks, and experimental results demonstrate its effectiveness.