Turbulent diffusion causes particles placed in proximity to separate. We investigate the required swimming efforts to maintain a particle close to its passively advected counterpart. We explore optimally balancing these efforts with the intended goal by developing and comparing a novel Physics-Informed Reinforcement Learning (PIRL) strategy with prescribed control (PC) and standard physics-agnostic Reinforcement Learning strategies. Our PIRL 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). This strategy is then compared with an analytically computed optimal PC policy derived from a stochastic optimal control formulation and standard physics-agnostic Actor-Critic type algorithms.

通过开发和比较一种新的物理知情强化学习策略，我们研究了维持颗粒与其被从被动运动中推动的近邻保持接近的所需游动努力。该策略采用了将神经网络参数化的评论家替换为一个解析导出的物理启发式函数（物理学家）的Actor-Physicist算法，并与解析计算的最优预设控制策略和标准物理不可知的Actor-Critic类型算法进行了比较。

在湍流中游泳的物理信息评论家型演员-评论家强化学习