# A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems

arXiv: https://arxiv.org/abs/2512.01167 · HTML: https://mlsanity.com/paper/2512.01167/

## Key facts

- Cited by 1 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 19 references that are in the map.
- First posted on arXiv 1 Dec 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, 4 of 4)

- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control](https://mlsanity.com/paper/2303.06110/) (2023)
- [Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control](https://mlsanity.com/paper/2409.12789/) (2024)
- [Optimal Lighting Control in Greenhouses Using Bayesian Neural Networks for Sunlight Prediction](https://mlsanity.com/paper/2205.03733/) (2022)

## Built on by (most cited first, 1 of 1)

- [Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey](https://mlsanity.com/paper/2604.07857/) (2026)

## Abstract (arXiv)

This study presents a reinforcement learning (RL)-based control strategy for adaptive lighting regulation in controlled environments using a low-power microcontroller. A model-free Q-learning algorithm was implemented to dynamically adjust the brightness of a Light-Emitting Diode (LED) based on real-time feedback from a light-dependent resistor (LDR) sensor. The system was trained to stabilize at 13 distinct light intensity levels (L1 to L13), with each target corresponding to a specific range within the 64-state space derived from LDR readings. A total of 130 trials were conducted, covering all target levels with 10 episodes each. Performance was evaluated in terms of convergence speed, steps taken, and time required to reach target states. Box plots and histograms were generated to analyze the distribution of training time and learning efficiency across targets. Experimental validation demonstrated that the agent could effectively learn to stabilize at varying light levels with minimal overshooting and smooth convergence, even in the presence of environmental perturbations. This work highlights the feasibility of lightweight, on-device RL for energy-efficient lighting control and sets the groundwork for multi-modal environmental control applications in resource-constrained agricultural systems.