Gym-preCICE: Reinforcement learning environments for active flow control
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711023001425 |
| _version_ | 1797679432132984832 |
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| author | Mosayeb Shams Ahmed H. Elsheikh |
| author_facet | Mosayeb Shams Ahmed H. Elsheikh |
| author_sort | Mosayeb Shams |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T23:14:30Z |
| format | Article |
| id | doaj.art-75e5d3918467438aa3fd46ae200bc3ee |
| institution | Directory Open Access Journal |
| issn | 2352-7110 |
| language | English |
| last_indexed | 2024-03-11T23:14:30Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj.art-75e5d3918467438aa3fd46ae200bc3ee2023-09-21T04:37:26ZengElsevierSoftwareX2352-71102023-07-0123101446Gym-preCICE: Reinforcement learning environments for active flow controlMosayeb Shams0Ahmed H. Elsheikh1Corresponding author.; Heriot-Watt University, Edinburgh, United KingdomHeriot-Watt University, Edinburgh, United KingdomActive 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.http://www.sciencedirect.com/science/article/pii/S2352711023001425Reinforcement learningActive flow controlGymnasiumOpenAI GympreCICE |
| spellingShingle | Mosayeb Shams Ahmed H. Elsheikh Gym-preCICE: Reinforcement learning environments for active flow control SoftwareX Reinforcement learning Active flow control Gymnasium OpenAI Gym preCICE |
| title | Gym-preCICE: Reinforcement learning environments for active flow control |
| title_full | Gym-preCICE: Reinforcement learning environments for active flow control |
| title_fullStr | Gym-preCICE: Reinforcement learning environments for active flow control |
| title_full_unstemmed | Gym-preCICE: Reinforcement learning environments for active flow control |
| title_short | Gym-preCICE: Reinforcement learning environments for active flow control |
| title_sort | gym precice reinforcement learning environments for active flow control |
| topic | Reinforcement learning Active flow control Gymnasium OpenAI Gym preCICE |
| url | http://www.sciencedirect.com/science/article/pii/S2352711023001425 |
| work_keys_str_mv | AT mosayebshams gymprecicereinforcementlearningenvironmentsforactiveflowcontrol AT ahmedhelsheikh gymprecicereinforcementlearningenvironmentsforactiveflowcontrol |