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