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

arXiv: https://arxiv.org/abs/2410.05336 · HTML: https://mlsanity.com/paper/2410.05336/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control](https://mlsanity.com/paper/2303.06110/) (2023)
- [Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control](https://mlsanity.com/paper/2108.11645/) (2021)
- [IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control](https://mlsanity.com/paper/2107.05464/) (2021)

## Built on by (most cited first, 2 of 2)

- [Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control](https://mlsanity.com/paper/2607.07365/) (2026)
- [Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses](https://mlsanity.com/paper/2607.11959/) (2026)

## 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.