Discrete Communication

Discrete communication in multi-agent systems focuses on enabling efficient information exchange between agents using limited, discrete messages, rather than continuous signals. Current research emphasizes developing algorithms and architectures, such as those based on reinforcement learning and clustering techniques, to learn effective discrete communication protocols in various settings, including multi-agent reinforcement learning and augmented reality applications. This research is significant because it addresses the limitations of continuous communication in terms of bandwidth, energy consumption, and interpretability, paving the way for more robust and scalable multi-agent systems in diverse fields. Improved methods for discretization and the development of novel algorithms that minimize communication overhead while maintaining performance are key areas of ongoing investigation.