arXiv:2606.02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully decentralized execution, ND-MARL incorporates the swarm communication graph into the decision process. Under a 2-Neighbor communication topology, each agent observes information of only two neighbors and outputs an action through a distributed policy. A high-level distributed consensus planner is trained using Multi-Age
The continuous advancements in AI and robotics, coupled with increasing interest in autonomous systems for various applications, drive the development of more sophisticated control mechanisms for multi-agent systems.
This research provides a more robust and scalable approach to coordinating drone swarms, which has significant implications for defense, logistics, and surveillance, moving beyond centralized control limitations.
The shift from conventional centralized or fully decentralized MARL to a network-distributed approach allows for greater resilience and efficiency in managing complex multi-quadcopter systems, enabling more sophisticated collective behaviors.
- · Defence contractors
- · Logistics companies
- · Robotics researchers
- · Software developers for autonomous systems
- · Systems relying on rudimentary drone control
- · Centralized drone management platforms
Improved coordination and scalability of drone swarms for complex tasks become feasible.
The tactical advantage of adversaries employing sophisticated drone swarms increases, necessitating countermeasures.
Urban air mobility and delivery systems could become more ubiquitous and reliable, integrating seamlessly into infrastructure.
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Read at arXiv cs.LG