Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (6): 264-270.DOI: 10.3778/j.issn.1002-8331.2009-0505

• Engineering and Applications • Previous Articles     Next Articles

Personalized Adjustment Method of Intelligent Lamp Based on Deep Reinforcement Learning

DENG Xin, NA Jun, ZHANG Handuo, WANG Yulin, ZHANG Bin   

  1. 1.School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
    2.School of Software, Northeastern University, Shenyang 110819, China
  • Online:2022-03-15 Published:2022-03-15



  1. 1.东北大学 计算机科学与工程学院,沈阳 110819
    2.东北大学 软件学院,沈阳 110819

Abstract: This paper proposes a deep reinforcement learning-based method for adjusting personalized smart lamp brightness. It considers the influence of both natural light and the user’s position on his/her actual visual brightness and sets the light intensity dynamically to meet the user’s personalized habits. After each automatic adjustment, according to whether the user takes further changes manually, positive or negative feedback will be collected to train the reinforcement learning model gradually fits the user’s usage habits. The experiment implements three algorithms of DQN, DDQN, and A3C, respectively, and presents the comparative analysis on the data set generated on the DIALux environment. The hardware and software implementation of the prototype system is also introduced.

Key words: deep reinforcement learning, personalized control, deep Q-network(DQN), double DQN(DDQN), asynchronous advantage actor-critic(A3C)

摘要: 提出一种基于深度强化学习的智能灯亮度个性化调节方法,综合考虑自然光亮度及用户位置对用户实际感受亮度的影响,动态计算并设置灯光亮度,以满足用户个性化使用习惯。在每次完成灯光亮度自动调节后,根据用户是否再次进行手动调节设定正、负反馈,训练强化学习模型逐渐拟合用户使用习惯。实验分别实现了DQN、DDQN和A3C三种算法,在基于DIALux环境产生的数据集上进行对比分析,并给出原型系统的软硬件实现。

关键词: 深度强化学习, 个性化控制, DQN算法, DDQN算法, A3C算法