GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915685045633024 |
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| author | van Laatum, Bart van Henten, Eldert J. Boersma, Sjoerd |
| author_facet | van Laatum, Bart van Henten, Eldert J. Boersma, Sjoerd |
| contents | This study presents GreenLight-Gym, a new, fast, open-source benchmark environment for developing reinforcement learning (RL) methods in greenhouse crop production control. Built on the state-of-the-art GreenLight model, it features a differentiable C++ implementation leveraging the CasADi framework for efficient numerical integration. GreenLight-Gym improves simulation speed by a factor of 17 over the original GreenLight implementation. A modular Python environment wrapper enables flexible configuration of control tasks and RL-based controllers. This flexibility is demonstrated by learning controllers under parametric uncertainty using two well-known RL algorithms. GreenLight-Gym provides a standardized benchmark for advancing RL methodologies and evaluating greenhouse control solutions under diverse conditions. The greenhouse control community is encouraged to use and extend this benchmark to accelerate innovation in greenhouse crop production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems van Laatum, Bart van Henten, Eldert J. Boersma, Sjoerd Systems and Control Machine Learning Optimization and Control This study presents GreenLight-Gym, a new, fast, open-source benchmark environment for developing reinforcement learning (RL) methods in greenhouse crop production control. Built on the state-of-the-art GreenLight model, it features a differentiable C++ implementation leveraging the CasADi framework for efficient numerical integration. GreenLight-Gym improves simulation speed by a factor of 17 over the original GreenLight implementation. A modular Python environment wrapper enables flexible configuration of control tasks and RL-based controllers. This flexibility is demonstrated by learning controllers under parametric uncertainty using two well-known RL algorithms. GreenLight-Gym provides a standardized benchmark for advancing RL methodologies and evaluating greenhouse control solutions under diverse conditions. The greenhouse control community is encouraged to use and extend this benchmark to accelerate innovation in greenhouse crop production. |
| title | GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems |
| topic | Systems and Control Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2410.05336 |