  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.

Machine Learning;Artificial Intelligence;Multiagent Systems