# Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control

arXiv: https://arxiv.org/abs/2505.23355 · HTML: https://mlsanity.com/paper/2505.23355/

## 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 12 of its 47 references that are in the map.
- First posted on arXiv 29 May 2025.

## 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, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges](https://mlsanity.com/paper/2105.12949/) (2021)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control](https://mlsanity.com/paper/2303.06110/) (2023)
- [Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment](https://mlsanity.com/paper/2007.03363/) (2020)
- [Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control](https://mlsanity.com/paper/2409.12789/) (2024)
- [Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning](https://mlsanity.com/paper/2303.06710/) (2023)
- [Improving interactive reinforcement learning: What makes a good teacher?](https://mlsanity.com/paper/1904.06879/) (2019)
- [Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control](https://mlsanity.com/paper/2108.11645/) (2021)
- [Intentionally-underestimated Value Function at Terminal State for Temporal-difference Learning with Mis-designed Reward](https://mlsanity.com/paper/2308.12772/) (2023)
- [Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores](https://mlsanity.com/paper/2307.05405/) (2023)

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

- [Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses](https://mlsanity.com/paper/2607.11959/) (2026)
- [Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2609.34966/) (2026)

## Abstract (arXiv)

Climate control is crucial for greenhouse production as it directly affects crop growth and resource use. Reinforcement learning (RL) has received increasing attention in this field, but still faces challenges, including limited training efficiency and high reliance on initial learning conditions. Interactive RL, which combines human (grower) input with the RL agent's learning, offers a potential solution to overcome these challenges. However, interactive RL has not yet been applied to greenhouse climate control and may face challenges related to imperfect inputs. Therefore, this paper aims to explore the possibility and performance of applying interactive RL with imperfect inputs into greenhouse climate control, by: (1) developing three representative interactive RL algorithms tailored for greenhouse climate control (reward shaping, policy shaping and control sharing); (2) analyzing how input characteristics are often contradicting, and how the trade-offs between them make grower's inputs difficult to perfect; (3) proposing a neural network-based approach to enhance the robustness of interactive RL agents under limited input availability; (4) conducting a comprehensive evaluation of the three interactive RL algorithms with imperfect inputs in a simulated greenhouse environment. The demonstration shows that interactive RL incorporating imperfect grower inputs has the potential to improve the performance of the RL agent. RL algorithms that influence action selection, such as policy shaping and control sharing, perform better when dealing with imperfect inputs, achieving 8.4% and 6.8% improvement in profit, respectively. In contrast, reward shaping, an algorithm that manipulates the reward function, is sensitive to imperfect inputs and leads to a 9.4% decrease in profit. This highlights the importance of selecting an appropriate mechanism when incorporating imperfect inputs.