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Active flow control (AFC) involves manipulating fluid flow over time toachieve a desired performance or efficiency. AFC, as a sequential optimisationtask, can benefit from utilising Reinforcement Learning (RL) for dynamicoptimisation. In this work, we introduce Gym-preCICE, a Python adapter fullycompliant with Gymnasium (formerly known as OpenAI Gym) API to facilitatedesigning and developing RL environments for single- and multi-physics AFCapplications. In an actor-environment setting, Gym-preCICE takes advantage ofpreCICE, an open-source coupling library for partitioned multi-physicssimulations, to handle information exchange between a controller (actor) and anAFC simulation environment. The developed framework results in a seamlessnon-invasive integration of realistic physics-based simulation toolboxes withRL algorithms. Gym-preCICE provides a framework for designing RL environmentsto model AFC tasks, as well as a playground for applying RL algorithms invarious AFC-related engineering applications.

Gym-preCICE: Reinforcement Learning Environments for Active Flow Control