Gym-preCICE: Reinforcement learning environments for active flow control

Active flow control (AFC) involves manipulating fluid over time to achieve a desired performance or efficiency. AFC, as sequential optimisation task, can benefit from utilising Reinforcement Learning (RL) for dynamic optimisation. In this work, we introduce Gym-preCICE, Python adapter fully compliant with Gymnasium API facilitate designing and developing RL environments single- multi-physics AFC applications. an actor–environment setting, Gym-preCICE takes advantage of preCICE, open-source coupling library partitioned simulations, handle information exchange between controller (actor) simulation environment. provides framework seamless non-invasive integration well playground applying algorithms in various AFC-related engineering