Gym-preCICE: Reinforcement learning environments for active flow control

Mosayeb Shams, Ahmed H. Elsheikh

Research output: Contribution to journalArticlepeer-review

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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 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. Gym-preCICE provides a framework for seamless non-invasive integration of RL and AFC, as well as a playground for applying RL algorithms in various AFC-related engineering applications.
Original languageEnglish
Article number101446
Early online date11 Jul 2023
Publication statusPublished - Jul 2023


  • Active flow control
  • Gymnasium
  • OpenAI Gym
  • Reinforcement learning
  • preCICE

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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