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 the symmetry of the state-action map, MARL strategy leverages a non-intuitive mechanism that prevents agents from becoming trapped in separate vortices, thereby enhancing rendezvous success. Additionally, a heuristic strategy is extracted from the learned strategy and also outperforms the naive strategy. Furthermore, a theoretical analysis demonstrates that fluid deformation impedes the rendezvous process. Large finite-time Lyapunov exponents identify where fluid effects separate adjacent agents, suggesting that targets should be planned in weak-deformation regions. Our findings reveal the important role that agent-fluid interactions play in multi-agent tasks and highlight the MARL capability to explore swarm intelligence in complex flow environments.

翻译：会合是多智能体系统中的关键任务，要求智能体协调在未指定位置相遇。然而，在流体环境中实现这一目标具有挑战性，因为尚不清楚智能体如何利用底层流体运动学促进趋同。本研究采用多智能体强化学习（MARL）方法，开发了涡旋流动中具有物理感知的会合策略。与智能体相互导航的朴素策略相比，MARL策略显著提升了会合成功率。MARL策略还展现出在变化涡旋强度、涡旋尺度及群体规模下的可迁移性。通过打破状态-动作映射的对称性，MARL策略利用一种反直觉机制防止智能体被困于不同涡旋中，从而增强会合效果。此外，从学习策略中提取的启发式策略也优于朴素策略。进一步理论分析表明，流体形变会阻碍会合过程。大有限时间李雅普诺夫指数可识别流体效应分离相邻智能体的区域，提示应选择弱形变区域规划目标点。本研究揭示了智能体-流体交互在多智能体任务中的重要作用，并凸显了MARL在复杂流场中探索群体智能的能力。