TY - JOUR
AU1 - Tovey, Samuel
AU2 - Lohrmann, Christoph
AU3 - Merkt, Tobias
AU4 - Zimmer, David
AU5 - Nikolaou, Konstantin
AU6 - Koppenhöfer, Simon
AU7 - Bushmakina, Anna
AU8 - Scheunemann, Jonas
AU9 - Holm, Christian
AB - Abstract: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 this https URL.
TI - SwarmRL: Building the Future of Smart Active Systems
JF - Physics
DO - 10.48550/arxiv.2404.16388
DA - 2024-04-25
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/swarmrl-building-the-future-of-smart-active-systems-a0m2nB7IUD
VL - 2024
IS - 2404
DP - DeepDyve
ER -