Gym-preCICE: Reinforcement Learning Environments for Active Flow Control

• URL: http://arxiv.org/abs/2305.02033v1
• Date: Wed, 3 May 2023 10:54:56 GMT
• Title: Gym-preCICE: Reinforcement Learning Environments for Active Flow Control
• Authors: Mosayeb Shams, Ahmed H. Elsheikh
• Abstract summary: Gym-preCICE is a Python adapter fully compliant with Gymnasium (formerly known as OpenAI Gym) API. Gym-preCICE takes advantage of preCICE, an open-source coupling library for partitioned multi-physics simulations. The framework results in a seamless integration of realistic physics-based simulation toolboxes with RL algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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