Multi-agent rendezvous in fluid flows via reinforcement learning
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 improve the rendezvous rate. MARL strategies also show transferability across varying vortex intensities, vortex scales, and swarm sizes. By breaking th...
| Authors | Bocheng Li, Jingran Qiu, Lihao Zhao |
|---|---|
| Primary category | cs.MA |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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