# Learning swimming escape patterns for larval fish under energy constraints

arXiv: https://arxiv.org/abs/2105.00771 · HTML: https://mlsanity.com/paper/2105.00771/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 72 references that are in the map.
- First posted on arXiv 3 May 2021.

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

- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Efficient collective swimming by harnessing vortices through deep reinforcement learning](https://mlsanity.com/paper/1802.02674/) (2018)
- [Remember and Forget for Experience Replay](https://mlsanity.com/paper/1807.05827/) (2018)
- [Flow Navigation by Smart Microswimmers via Reinforcement Learning](https://mlsanity.com/paper/1701.08848/) (2017)
- [A statistical learning strategy for closed-loop control of fluid flows](https://mlsanity.com/paper/1604.03392/) (2016)
- [Deep-Reinforcement-Learning for Gliding and Perching Bodies](https://mlsanity.com/paper/1807.03671/) (2018)

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

- [Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning](https://mlsanity.com/paper/2404.02171/) (2024)
- [Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning](https://mlsanity.com/paper/2511.07564/) (2025)
- [A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows](https://mlsanity.com/paper/2505.05525/) (2025)

## Abstract (arXiv)

Swimming organisms can escape their predators by creating and harnessing unsteady flow fields through their body motions. Stochastic optimization and flow simulations have identified escape patterns that are consistent with those observed in natural larval swimmers. However, these patterns have been limited by the specification of a particular cost function and depend on a prescribed functional form of the body motion. Here, we deploy reinforcement learning to discover swimmer escape patterns for larval fish under energy constraints. The identified patterns include the C-start mechanism, in addition to more energetically efficient escapes. We find that maximizing distance with limited energy requires swimming via short bursts of accelerating motion interlinked with phases of gliding. The present, data efficient, reinforcement learning algorithm results in an array of patterns that reveal practical flow optimization principles for efficient swimming and the methodology can be transferred to the control of aquatic robotic devices operating under energy constraints.