# A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning
## 1 Introduction
When the number of indoor optimization targets increases, however, the implementation of intelligent optimization algorithms will make the computation more intensive, which will have an effect on the system's real-time control. It is therefore important to find an algorithm that can combine real time and scalability to achieve uniform illumination. Shading systems are a type of passive energy-saving technology used to shield buildings from intense solar radiation [16], and they can influence the temperature-controlled equipment burdens of buildings as well as indoor thermal and visual comfort [17]. However, static shading systems do not adapt well to varying climatic conditions due to a lack of flexibility in the control process [18; 19; 20]; so, they do not reduce lighting energy consumption very effectively. [PERSON] et al. [21] and [PERSON] et al. [22] found that a blinds system can reduce cooling loads by 12 to 35% in the summer while enhancing visual comfort. [PERSON] and [PERSON]'s [23] rational installation of automated shading devices is therefore not only a trend but also an excellent option for improved daylight performance and energy savings. Daylight Connection Control Systems (DLCS) are systems that control both daylight and electric illumination [24]. [PERSON] et al., [PERSON] et al. and [PERSON] et al. [25; 26; 27] report that the implementation of DLCS to maximize the use of daylight has a high potential to reduce lighting energy consumption. In addition, with the advancement of network technology, the Internet of Things (IoT), which connects multiple devices via the Internet, has been extensively adopted in a variety of fields, and there are promising research outcomes in intelligent lighting [28; 29; 30; 31]. This offers great opportunities for scalability of control systems, leading to a move towards intelligent and networked lighting systems today. Despite the fact that numerous researchers have proposed related lighting systems, there are some systems that need to find the optimal dimming coefficients once again before dimming, and their computation and time will increase with the number of indoor luminaires and detection points, which will make the real-time performance of the system deteriorate. In addition, most lighting systems are based on a centralized framework, which makes installation and maintenance difficult, as they require code remodification and more work to be carried out when the application place changes, e.g., when applying the lighting system to several rooms with different numbers of windows. Therefore, in order to reduce the amount of computation during the application of the system, improve the response speed of the system, and realize the plug-and-play function of the system, this paper proposes a distribution-based intelligent control method combining dynamic shading and lighting. The method reduces energy consumption, improves user comfort and experience, and is easy to install and maintain while meeting personal illumination requirements. The main contributions of the paper are summarized as follows:  
* First, a fully distributed intelligent lighting system framework is proposed, and a set of general-purpose applications are developed on the distributed framework to achieve plug-and-play and flexible expansion. * Second, a shading system is developed that adaptively adjusts the amount of natural light radiation based on room temperature and brightness using fuzzy control logic. * Third, based on deep reinforcement learning, a lighting control method that comprehensively considers uniform illumination and natural light is proposed. The remainder of this paper is organized as follows. In the Section 2, we proposed a distributed smart lighting control system framework, which employed blind fuzzy control algorithm and illumination uniform control algorithm based on DDPG. Then, the control prototype was presented in Section 3. Section 4 provided the tests and results of strong and weak natural light scenarios.