# Guided Deep Reinforcement Learning for Articulated Swimming Robots

arXiv: https://arxiv.org/abs/2301.13072 · HTML: https://mlsanity.com/paper/2301.13072/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [Enhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning](https://mlsanity.com/paper/2506.04627/) (2025)
- [Training microrobots to swim by a large language model](https://mlsanity.com/paper/2402.00044/) (2024)
- [Fine Tuning Swimming Locomotion Learned from Mosquito Larvae](https://mlsanity.com/paper/2412.02702/) (2024)

## Abstract (arXiv)

Deep reinforcement learning has recently been applied to a variety of robotics applications, but learning locomotion for robots with unconventional configurations is still limited. Prior work has shown that, despite the simple modeling of articulated swimmer robots, such systems struggle to find effective gaits using reinforcement learning due to the heterogeneity of the search space. In this work, we leverage insight from geometric models of these robots in order to focus on promising regions of the space and guide the learning process. We demonstrate that our augmented learning technique is able to produce gaits for different learning goals for swimmer robots in both low and high Reynolds number fluids.