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SwarmRL: Building the Future of Smart Active Systems

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arxiv 2404.16388 v1 pith:TC2AUTFI submitted 2024-04-25 cs.RO cond-mat.softcs.AIcs.MAphysics.bio-ph

classification cs.ROcond-mat.softcs.AIcs.MAphysics.bio-ph
keywords swarmrlcontrolactivegithubmodelsresearchaccelerateapproaches
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Ant swarm functional control via stigmergic Reinforcement Learning agents

    physics.soc-ph 2026-07 conditional novelty 6.0 of 10

    Reinforcement-learned stigmergic agents shift the order–disorder phase boundary of the ant swarm model, producing trail formation in regimes previously dominated by randomness.

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