Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward
arXiv:2605.24992v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) has shown wide applicability in collaborative systems such as autonomous driving and smart cities for its ability of learning through interaction. With the recent development of drone networks, researchers have also applied MARL to address the trajectory planning problems. However, the dynamic environment and the limited battery capacity are still challenging for using MARL to achieve efficient collaborative task execution. In this paper, we propose an energy-aware MARL model as an attempt to tackle the
The proliferation of drone technology and the increasing maturity of multi-agent reinforcement learning make the convergence of these fields timely for addressing complex, mission-oriented tasks.
This research directly addresses the critical energy limitations and operational complexities of drone networks, enabling more robust and autonomous deployment in various sectors.
The ability to manage energy consumption intelligently in multi-drone systems allows for extended operational periods and more sophisticated collaborative missions.
- · Defence contractors
- · Logistics and delivery companies
- · AI software developers
- · Drone manufacturers
- · Traditional surveillance methods
- · Human-crewed monitoring services
Increased efficiency and duration of drone-based operations requiring coordinated autonomy.
Accelerated adoption of autonomous drone swarms for complex tasks like reconnaissance, infrastructure inspection, and last-mile delivery.
Reduced human oversight requirements for large-scale drone deployments, leading to new regulatory and ethical considerations for autonomous systems.
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