Biomedical applications such as targeted drug delivery, microsurgery or
sensing rely on reaching precise areas within the body in a minimally invasive
way. Artificial bacterial flagella (ABFs) have emerged as potential tools for
this task by navigating through the circulatory system. While the control and
swimming characteristics of ABFs is understood in simple scenarios, their
behavior within the bloodstream remains unclear. We conduct simulations of ABFs
evolving in the complex capillary networks found in the human retina. The ABF
is robustly guided to a prescribed target by a reinforcement learning agent
previously trained on a reduced order model.

以仿生细菌鞭毛为工具，在复杂的人类视网膜毛细血管网络中，通过强化学习机制将其可靠地引导到预定目标，以实现生物医学应用的靶向药物输送、微创手术及感知等。

磁性微游动器在深度强化学习下的高保真毛细血管路径规划

Path planning of magnetic microswimmers in high-fidelity simulations of  capillaries with deep reinforcement learning

Micro robotics is quickly emerging to be a promising technological solution
to many medical treatments with focus on targeted drug delivery. They are
effective when working in swarms whose individual control is mostly infeasible
owing to their minute size. Controlling a number of robots with a single
controller is thus important and artificial intelligence can help us perform
this task successfully. In this work, we use the Reinforcement Learning (RL)
algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization
(RPO) to navigate a swarm of 4, 9 and 16 microswimmers under hydrodynamic
effects, controlled by their orientation, towards a circular absorbing target.
We look at both PPO and RPO performances with limited state information
scenarios and also test their robustness for random target location and size.
We use curriculum learning to improve upon the performance and demonstrate the
same in learning to navigate a swarm of 25 swimmers and steering the swarm to
exemplify the manoeuvring capabilities of the RL model.

本研究使用强化学习算法 (PPO 和 RPO) 来控制微型机器人群在水力影响下到达目标。研究表明课程学习可以提高性能，并展示了在控制 25 个游泳器协同机动方面 RL 模型的能力。