# SwarmRL: Building the Future of Smart Active Systems

arXiv: https://arxiv.org/abs/2404.16388 · HTML: https://mlsanity.com/paper/2404.16388/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 74 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://mlsanity.com/paper/1702.08892/) (2017)
- [Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering](https://mlsanity.com/paper/2202.01284/) (2022)
- [Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number](https://mlsanity.com/paper/2106.08609/) (2021)
- [Towards a Phenomenological Understanding of Neural Networks: Data](https://mlsanity.com/paper/2305.00995/) (2023)
- [Optimal active particle navigation meets machine learning](https://mlsanity.com/paper/2303.05558/) (2023)

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

- [Accelerating PDE Surrogates via RL-Guided Mesh Optimization](https://mlsanity.com/paper/2603.02066/) (2026)
- [Controlling dynamics of stochastic systems with deep reinforcement learning](https://mlsanity.com/paper/2502.18111/) (2025)
- [Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries](https://mlsanity.com/paper/2606.26154/) (2026)

## Abstract (arXiv)

This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL.