mlsanityData to 14 Oct 2026

SwarmRL: Building the Future of Smart Active Systems

arXiv 2404.16388 ↗First posted 25 Apr 2024cs.RO cond-mat.soft cs.AIcited by 3

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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.

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

14 of 74 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  4. 2018Exploration by Random Network Distillationcited by 993
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  6. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
  7. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
  8. 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
  9. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
  10. 2017Bridging the Gap Between Value and Policy Based Reinforcement Learningcited by 182
  11. 2022Dr.Jit: A Just-In-Time Compiler for Differentiable Renderingcited by 16
  12. 2021Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds numbercited by 7
2 more
  1. 2023Towards a Phenomenological Understanding of Neural Networks: Datacited by 4
  2. 2023Optimal active particle navigation meets machine learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for Active Matter
  2. 2026Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms ↗
  3. 2025Machine Learning-based Optimal Control for Colloidal Self-Assembly ↗
  4. 2024Counterfactual rewards promote collective transport using individually controlled swarm microrobots
  5. 2025An effective control of large systems of active particles: An application to evacuation problem
  6. 2026ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing
  7. 2025Physical Embodiment Enables Information Processing Beyond Explicit Sensing in Active Matter ↗
  8. 2023Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration