Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existing multi-agent reinforcement learning (MARL) algorithms often provide agents with a binary choice: either transmitting a fixed number of bytes or no information at all. This limitation hinders the ability to effectively utilize the available bandwidth. To overcome this challenge, we present the Dynamic Size Message Scheduling (DSMS) method, which introduces a finer-grained approach to scheduling by considering the actual size of the information to be exchanged. Our contribution lies in adaptively adjusting message sizes using Fourier transform-based compression techniques, enabling agents to tailor their messages to match the allocated bandwidth while striking a balance between information loss and transmission efficiency. Receiving agents can reliably decompress the messages using the inverse Fourier transform. Experimental results demonstrate that DSMS significantly improves performance in multi-agent cooperative tasks by optimizing the utilization of bandwidth and effectively balancing information value.

翻译：通信在多智能体系统中扮演着至关重要的角色，能够促进协作与协调。然而，在带宽受限的现实场景中，现有的多智能体强化学习算法通常仅为智能体提供二元选择：要么传输固定字节数的信息，要么不传输任何信息。这种局限性阻碍了有效利用可用带宽的能力。为克服这一挑战，我们提出了动态大小消息调度方法，该方法通过考虑待交换信息的实际大小，引入了一种更细粒度的调度方式。我们的贡献在于利用基于傅里叶变换的压缩技术自适应地调整消息大小，使智能体能够定制消息以匹配分配的带宽，同时在信息损失与传输效率之间取得平衡。接收智能体可通过逆傅里叶变换可靠地解压消息。实验结果表明，DSMS通过优化带宽利用并有效平衡信息价值，显著提升了多智能体协作任务的性能。