🤖 AI Summary
This work addresses the challenge of multi-agent rendezvous in complex fluid environments, where agents often become trapped in separated states due to vortex dynamics. The authors propose a fluid physics-informed multi-agent reinforcement learning (MARL) strategy that breaks state-action mapping symmetry to uncover non-intuitive cooperative mechanisms, effectively avoiding vortex-induced traps. The approach demonstrates strong generalization across varying vortex intensities, spatial scales, and group sizes, and yields heuristic policies that outperform baseline methods. Experimental results show that the proposed MARL strategy significantly improves rendezvous success rates. Furthermore, theoretical analysis reveals that fluid deformation impedes the rendezvous process, suggesting that regions with weaker deformation are more suitable as rendezvous targets.
📝 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 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.