Gym-preCICE: Reinforcement Learning Environments for Active Flow Control
This work provides a tool for researchers and engineers to apply RL in active flow control, but it is incremental as it builds on existing frameworks like Gymnasium and preCICE.
The authors tackled the challenge of integrating reinforcement learning with active flow control simulations by developing Gym-preCICE, a Python adapter that enables seamless coupling between RL algorithms and physics-based simulations using the preCICE library.
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.