arXiv:2605.21085v1 Announce Type: cross Abstract: Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe bandwidth constraints. Many communication architectures still expose a coupled bottleneck in which a shared latent representation is used for both policy execution and inter-agent communication. Consequently, reducing message size directly limits the policy's latent space, often leading to significant performance degradation. We address this with two contributions. Fir
The proliferation of MARL systems in real-world scenarios, particularly in domains like drone swarms, highlights the critical need for robust solutions under practical constraints such as limited bandwidth.
This research addresses a fundamental bottleneck in multi-agent systems, improving their resilience and applicability in challenging environments, which is crucial for advancing autonomous operations.
The proposed decoupling of communication from policy execution allows for more efficient and robust MARL systems, mitigating performance degradation previously caused by bandwidth limitations.
- · Defence contractors
- · Robotics companies
- · Logistics and supply chain operators
- · AI research and development
- · Companies reliant on high-bandwidth, centralized MARL
- · Legacy communication infrastructure providers
More widespread deployment of MARL in bandwidth-constrained environments becomes feasible.
This enables faster development and adoption of autonomous systems for complex tasks like disaster response and defense.
Increased autonomy in these sectors could reduce human risk and potentially reshape operational doctrines.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG