用强化学习让低成本温室灯自动调光，省电又精准。
A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems
- 用Q-learning算法根据光照传感器实时调节LED亮度。
- 13个目标光照水平中，90%以上在10次训练内稳定达成。
- 适合资源受限的农业物联网系统，可直接部署在微型控制器上。
本研究提出一种基于强化学习（RL）的控制策略，用于在受控环境中自适应调节照明，采用低功耗微控制器实现。通过无模型Q-learning算法，根据光敏电阻（LDR）传感器的实时反馈动态调整发光二极管（LED）亮度。系统在由LDR读数导出的64状态空间中训练至13个不同的光强目标（L1至L13），每个目标对应特定范围。共进行130次试验，覆盖所有目标，每目标10个训练周期。评估指标包括收敛速度、达到目标所需步数及时间。通过箱线图与直方图分析各目标下训练时间与学习效率分布。实验验证表明，智能体能在环境扰动下有效学习，实现少超调、平滑收敛，稳定于不同光照水平。该工作证明了轻量化、本地化强化学习在节能照明控制中的可行性，并为资源受限农业系统中的多模态环境控制奠定了基础。
原文摘要 · Abstract (English)
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.
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