mlsanityData to 14 Oct 2026

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

arXiv 2505.23355 ↗First posted 29 May 2025cs.LG math.OCcited by 2

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

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.

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.

Builds on

12 of 47 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control19 Sep

Other references · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 84
  3. 2021A Survey on Interactive Reinforcement Learning: Design Principles and Open Challengescited by 41
  4. 2023Active learning for data streams: a surveycited by 29
  5. 2023Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Controlcited by 12
  6. 2020Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environmentcited by 10
  7. 2023Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learningcited by 9
  8. 2019Improving interactive reinforcement learning: What makes a good teacher?cited by 8
  9. 2021Robust Model-based Reinforcement Learning for Autonomous Greenhouse Controlcited by 6
  10. 2023Intentionally-underestimated Value Function at Terminal State for Temporal-difference Learning with Mis-designed Rewardcited by 5
  11. 2023Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scorescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhousescited by 0
  2. 2026Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
  2. 2025RL-Guided MPC for Autonomous Greenhouse Control
  3. 2024A Comparative Study of Deep Reinforcement Learning for Crop Production Management
  4. 2026Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control
  5. 2024Breeding Programs Optimization with Reinforcement Learning
  6. 2025A Teacher-Student MPC-PPO Coupled Reinforcement Learning Framework for Winter Temperature Control of Solar Greenhouses in Northern China
  7. 2024Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties
  8. 2025A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems