Turbulent diffusion causes particles placed in proximity to separate. We investigate the required swimming efforts to maintain an active particle close to its passively advected counterpart. We explore optimally balancing these efforts by developing a novel physics-informed reinforcement learning strategy and comparing it with prescribed control and physics-agnostic reinforcement learning strategies. Our scheme, coined the actor-physicist, is an adaptation of the actor-critic algorithm in which the neural network parameterized critic is replaced with an analytically derived physical heuristic function, the physicist. We validate the proposed physics-informed reinforcement learning approach through extensive numerical experiments in both synthetic BK and more realistic Arnold-Beltrami-Childress flow environments, demonstrating its superiority in controlling particle dynamics when compared to standard reinforcement learning methods.

翻译：湍流扩散会导致初始位置邻近的粒子相互分离。本研究探讨了为使一个主动游泳粒子保持在与其被动平流运动的对应粒子附近所需的游泳努力。我们通过开发一种新颖的基于物理信息的强化学习策略，并将其与预设控制策略及物理无关的强化学习策略进行比较，来探索如何最优地平衡这些努力。我们提出的方案，命名为“演员-物理学家”，是对演员-评论家算法的一种改进，其中由神经网络参数化的评论家被替换为一个通过解析推导得到的物理启发式函数，即“物理学家”。我们在合成的BK流场和更接近现实的Arnold-Beltrami-Childress流场环境中进行了广泛的数值实验，验证了所提出的基于物理信息的强化学习方法。实验结果表明，与标准强化学习方法相比，该方法在控制粒子动力学方面具有优越性。