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。

利用强化学习导航微型机器人群体以进行定向输送

Navigation of micro-robot swarms for targeted delivery using  reinforcement learning

In this work we propose a reinforcement learning (RL) framework that controls
the density of a large-scale swarm for engaging with adversarial swarm attacks.
Although there is a significant amount of existing work in applying artificial
intelligence methods to swarm control, analysis of interactions between two
adversarial swarms is a rather understudied area. Most of the existing work in
this subject develop strategies by making hard assumptions regarding the
strategy and dynamics of the adversarial swarm. Our main contribution is the
formulation of the swarm to swarm engagement problem as a Markov Decision
Process and development of RL algorithms that can compute engagement strategies
without the knowledge of strategy/dynamics of the adversarial swarm. Simulation
results show that the developed framework can handle a wide array of
large-scale engagement scenarios in an efficient manner.

本文提出了一种强化学习 (RL) 框架，用于控制一大规模群体的密度以对抗敌对群体攻击。通过将群体对抗问题建模为马尔科夫决策过程并开发出 RL 算法，可在不了解敌对群体策略 / 动态的情况下计算出对抗策略，模拟结果表明，所开发的框架可以以高效的方式处理各种大规模对抗场景。