arXiv:2606.29126v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vectors detached from the structure of the observations they summarize. This design overlooks an important source of inductive bias in many cooperative environments, where observations naturally follow a hierarchy such as groups and entities. We propose \textsc{HiComm}, a plug-in communication module that grounds messages in the sender's hierarchical observation. \textsc{Hi
The continuous evolution of multi-agent reinforcement learning (MARL) research demands more efficient communication protocols as complexity and scale increase.
Sophisticated communication architectures are critical for scaling AI agents, enabling them to handle more complex, real-world tasks with partial observability.
This research introduces a method for hierarchical communication that aligns messages with observation structure, potentially leading to more robust and explainable multi-agent systems.
- · AI researchers
- · Developers of multi-agent systems
- · Companies using AI for complex coordination tasks
- · Systems reliant on flat communication protocols
Improved performance and efficiency in multi-agent reinforcement learning applications through better communication.
Accelerated development of more capable and autonomous AI agents for various industries.
The emergence of new AI applications previously constrained by limitations in multi-agent coordination and communication.
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Read at arXiv cs.AI