mlsanityData to 14 Oct 2026

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

arXiv 2410.05336 ↗First posted 6 Oct 2024eess.SY cs.LG cs.SYcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 20 references that are in the map. First posted on arXiv 6 Oct 2024.

Builds on

4 of 20 references.

References · 4

  1. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 170
  2. 2023Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Controlcited by 12
  3. 2021Robust Model-based Reinforcement Learning for Autonomous Greenhouse Controlcited by 6
  4. 2021IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Controlcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive controlcited by 0
  2. 2026Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhousescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control
  2. 2025RL-Guided MPC for Autonomous Greenhouse Control
  3. 2026Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks
  4. 2025A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems
  5. 2024Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
  6. 2025WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies
  7. 2025A Teacher-Student MPC-PPO Coupled Reinforcement Learning Framework for Winter Temperature Control of Solar Greenhouses in Northern China
  8. 2024A Comparative Study of Deep Reinforcement Learning for Crop Production Management