Reinforcement learning for autonomous navigation of swimmers in turbulent flow

Efficient navigation of autonomous swimmers is crucial for numerous applications, ranging from synthetic microswimmers for targeted drug delivery to oceanographic buoys for ocean/ weather monitoring and spilled oil tracking. In this paper, we study autonomous navigation within a turbulent flow, which is challenging due to the nonlinearity of the flow. In particular, a deep reinforcement learning (RL) technique is employed to train an autonomous swimmer to navigate efficiently towards a target in a two-dimensional turbulent flow by changing their flow direction. A neural network, that maps the measured state to possible actions, is trained by repeated experience with the turbulent flow environment. The resulting controller is compared to a 'naive' swimmer which is always directly oriented towards the target regardless of the underlying flow. The RL swimmer performs on average significantly better than the naive swimmer and learns to utilize vortical motion to its advantage by aligning its swimming direction with the flow direction.