打造高效温室控制强化学习基准环境，加速智能种植研究
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
- 基于可微分C++与CasADi实现，模拟速度提升17倍
- 模块化Python接口支持多种控制任务配置与算法测试
- 适合研究温室调控的强化学习学者与农业智能化开发者
本研究提出GreenLight-Gym，一个新型、快速、开源的强化学习（RL）基准环境，用于温室作物生产控制方法的研发。该环境基于前沿的GreenLight模型，采用可微分的C++实现，并利用CasADi框架进行高效数值积分，相比原始GreenLight实现，仿真速度提升17倍。通过模块化的Python封装，支持灵活配置控制任务和基于RL的控制器。实验展示了在参数不确定性下使用两种经典强化学习算法学习控制器的能力。GreenLight-Gym为推进强化学习方法并评估不同条件下温室控制方案提供了标准化基准。研究鼓励温室控制领域学者使用并扩展该基准，以加速温室作物生产的创新。
原文摘要 · Abstract (English)
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.
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