GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems

TL;DR

GreenLight-Gym is a fast, open-source reinforcement learning benchmark environment for greenhouse control, enabling efficient development and testing of RL algorithms in agricultural settings.

Contribution

It introduces GreenLight-Gym, a modular, high-speed simulation platform built on GreenLight, facilitating RL research in greenhouse crop production control.

Findings

Simulation speed increased by a factor of 17

RL controllers successfully learned under parametric uncertainty

Provides a standardized benchmark for RL in greenhouse control

Abstract

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.