  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.
