Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.16388 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911852961726464 |
|---|---|
| author | Tovey, Samuel Lohrmann, Christoph Merkt, Tobias Zimmer, David Nikolaou, Konstantin Koppenhöfer, Simon Bushmakina, Anna Scheunemann, Jonas Holm, Christian |
| author_facet | Tovey, Samuel Lohrmann, Christoph Merkt, Tobias Zimmer, David Nikolaou, Konstantin Koppenhöfer, Simon Bushmakina, Anna Scheunemann, Jonas Holm, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16388 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SwarmRL: Building the Future of Smart Active Systems Tovey, Samuel Lohrmann, Christoph Merkt, Tobias Zimmer, David Nikolaou, Konstantin Koppenhöfer, Simon Bushmakina, Anna Scheunemann, Jonas Holm, Christian Robotics Soft Condensed Matter Artificial Intelligence Multiagent Systems Biological Physics 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. |
| title | SwarmRL: Building the Future of Smart Active Systems |
| topic | Robotics Soft Condensed Matter Artificial Intelligence Multiagent Systems Biological Physics |
| url | https://arxiv.org/abs/2404.16388 |