arXiv:2606.11274v1 Announce Type: cross Abstract: Rendezvous is a critical task for multi-agent systems, requiring agents to coordinate to meet at an unspecified location. However, achieving this in fluid environments presents a challenge, as it remains unclear how agents can exploit underlying fluid kinematics to facilitate convergence. In this study, we adopt a multi-agent reinforcement learning (MARL) approach to develop physics-informed rendezvous strategies in vortical flows. Compared to a naive strategy, where agents navigate toward their counterparts, MARL strategies significantly impro
The increasing sophistication of multi-agent reinforcement learning techniques combined with growing computational power allows for practical application to complex physical environments such as fluid dynamics.
This research demonstrates a significant step towards autonomous coordination of agents in challenging real-world physics-driven environments, paving the way for advanced robotics and autonomous systems.
The ability of multi-agent systems to self-organize and achieve complex tasks within dynamic fluid environments moves from theoretical exploration to practical, physics-informed strategy development.
- · AI/ML researchers
- · Robotics industry
- · Defence industry
- · Oceanography/Marine exploration
- · Traditional control systems
- · Systems relying on perfect environmental modeling
Autonomous underwater vehicles or aerial drones gain enhanced coordination capabilities in complex environments.
Improved efficiency and safety for tasks like environmental monitoring, search and rescue, and resource exploration in challenging fluid conditions.
The development of highly adaptive and resilient multi-agent swarms that operate without constant human oversight in unpredictable environments.
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Read at arXiv cs.LG