Jan 1, 2024
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 amount of data or no information at all. This rigid communication strategy hinders the ability to effectively utilize bandwidth. To overcome this challenge, we present the Dynamic Size Message Scheduling (DSMS) method, which introduces finer-grained communication scheduling by considering the actual size of the information being exchanged. Our approach lies in adapting message sizes using Fourier transform-based compression techniques with clipping, enabling agents to tailor their messages to match the allocated bandwidth according to importance weights. This method realizes a balance between information loss and bandwidth utilization. Receiving agents reliably decompress the messages using the inverse Fourier transform. We evaluate DSMS in cooperative tasks where the agent has partial observability. Experimental results demonstrate that DSMS significantly improves performance by optimizing the utilization of bandwidth and effectively balancing information importance.
Jan 1, 2024
Jan 1, 2023
Jan 1, 2022