# Navigation of a Three-Link Microswimmer via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.00084 · HTML: https://mlsanity.com/paper/2506.00084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Flow Navigation by Smart Microswimmers via Reinforcement Learning](https://mlsanity.com/paper/1701.08848/) (2017)
- [Machine learning strategies for path-planning microswimmers in turbulent flows](https://mlsanity.com/paper/1910.01728/) (2019)
- [Learning to swim in potential flow](https://mlsanity.com/paper/2009.14280/) (2020)
- [Gait learning for soft microrobots controlled by light fields](https://mlsanity.com/paper/1809.03225/) (2018)
- [Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning](https://mlsanity.com/paper/2201.05599/) (2022)

## Built on by (most cited first, 1 of 1)

- [Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number](https://mlsanity.com/paper/2507.18849/) (2025)

## Abstract (arXiv)

Motile microorganisms develop effective swimming gaits to adapt to complex biological environments. Translating this adaptability to smart microrobots presents significant challenges in motion planning and stroke design. In this work, we explore the use of reinforcement learning (RL) to develop stroke patterns for targeted navigation in a three-link swimmer model at low Reynolds numbers. Specifically, we design two RL-based strategies: one focusing on maximizing velocity (Velocity-Focused Strategy) and another balancing velocity with energy consumption (Energy-Aware Strategy). Our results demonstrate how the use of different reward functions influences the resulting stroke patterns developed via RL, which are compared with those obtained from traditional optimization methods. Furthermore, we showcase the capability of the RL-powered swimmer in adapting its stroke patterns in performing different navigation tasks, including tracing complex trajectories and pursuing moving targets. Taken together, this work highlights the potential of reinforcement learning as a versatile tool for designing efficient and adaptive microswimmers capable of sophisticated maneuvers in complex environments.