# Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number

arXiv: https://arxiv.org/abs/2106.08609 · HTML: https://mlsanity.com/paper/2106.08609/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Efficient collective swimming by harnessing vortices through deep reinforcement learning](https://mlsanity.com/paper/1802.02674/) (2018)
- [Flow Navigation by Smart Microswimmers via Reinforcement Learning](https://mlsanity.com/paper/1701.08848/) (2017)
- [Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning](https://mlsanity.com/paper/1907.08591/) (2019)
- [Machine learning strategies for path-planning microswimmers in turbulent flows](https://mlsanity.com/paper/1910.01728/) (2019)
- [Neural Replicator Dynamics](https://mlsanity.com/paper/1906.00190/) (2019)
- [Visual Hide and Seek](https://mlsanity.com/paper/1910.07882/) (2019)

## Built on by (most cited first, 7 of 7)

- [Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning](https://mlsanity.com/paper/2307.00994/) (2023)
- [SwarmRL: Building the Future of Smart Active Systems](https://mlsanity.com/paper/2404.16388/) (2024)
- [Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2212.09612/) (2022)
- [Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence](https://mlsanity.com/paper/2406.10242/) (2024)
- [Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning](https://mlsanity.com/paper/2404.02171/) (2024)
- [Mixing Artificial and Natural Intelligence: From Statistical Mechanics to AI and Back to Turbulence](https://mlsanity.com/paper/2403.17993/) (2024)
- [Multi-agent rendezvous in fluid flows via reinforcement learning](https://mlsanity.com/paper/2606.11274/) (2026)

## Abstract (arXiv)

We consider a model of two competing microswimming agents engaged in a pursue-evasion task within a low-Reynolds-number environment. Agents can only perform simple maneuvers and sense hydrodynamic disturbances, which provide ambiguous (partial) information about the opponent's position and motion. We frame the problem as a zero-sum game: The pursuer has to capture the evader in the shortest time, while the evader aims at deferring capture as long as possible. We show that the agents, trained via adversarial reinforcement learning, are able to overcome partial observability by discovering increasingly complex sequences of moves and countermoves that outperform known heuristic strategies and exploit the hydrodynamic environment.