SwarmRL: Building the Future of Smart Active Systems
This work addresses the need for accessible tools in micro-robotics research, but it is incremental as it builds on existing methods without introducing new paradigms.
The authors introduced SwarmRL, a Python package for studying intelligent active particles, which provides an interface to control microscopic colloids using classical control and deep reinforcement learning, aiming to streamline micro-robotic control research and bridge experimental and simulation gaps.
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.