# A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning
## 1 Introduction  
With the accelerated development of modern technology, the quantity and environmental quality of a variety of buildings are increasing, and their energy consumption continues to rise, which will place a tremendous strain on the natural environment. The construction industry accounts for a significant portion of a country's total annual energy consumption, comprising roughly 40% of total energy consumption and 30% of greenhouse gas emissions [1]. Over 19% of the world's total electrical energy consumption is currently used for lighting. Even though the electrification of vehicles and heating systems may reduce the proportion of electrical energy consumed by lighting, the absolute figures for lighting energy consumption will continue to rise due to the increase in the number of buildings [2]. In office buildings, artificial illumination can consume up to 40% of total energy consumption [3]. Simultaneously, the demand for convenience and intelligence in the indoor environment is increasing. Luminaire technology advancements provide a potent instrument for intelligent illumination control. Light-Emitting Diodes (LEDs) are the most efficient light source presently available, and the cumulative energy demand during the trial phase will be reduced by 43-52% with LED lamps versus compact fluorescent lamps [2]. With the development of LED quality and the introduction of intelligent lighting systems, lighting technology has made significant strides forward. Human-centered lighting (HCL) that prioritizes human well-being and quality of life should be the futureof lighting [4]. Under the premise of satisfying lighting requirements, the current intelligent lighting system's primary objective is to further conserve energy, reduce emissions, and improve the user experience. Lighting uniformity is the degree of difference in lighting distribution between a specific area of the field of view and adjacent areas (e.g., between the working surface and the area adjacent to the working surface). The smaller the degree of difference, the better it is; the greater the degree of difference, the worse it is. As the luminous intensity distribution of LEDs approximates the Lambert distribution, the uneven distribution of luminous intensity in the illuminated area tends to cause glare or shadows, which affects visual operation and reduces visual operational performance. The human eye is prone to fatigue and even myopia when exposed to such uneven light for long periods of time. To avoid such problems, LEDs need to undergo proper optical design in order to achieve uniform illumination; therefore, the light uniformity of LED lighting is also an important issue that should be paid attention to [5; 6; 7]. Traditional lighting control systems, such as those based on Proportional Integral Derivative (PID) feedback control [8] or fuzzy controller control [9; 10], can modify room illuminance according to environmental changes and reduce energy consumption. However, the application of PID control and fuzzy control makes it difficult to find an optimal set of luminaire switch-on levels to control multiple luminaires so that the illuminance values at multiple test points are consistent, making it difficult to achieve uniform illumination. Based on neural network models [11; 12], a method for the predictive control of multiple targets in public illumination spaces capable of balancing real time and robustness is proposed. There is a substantial opportunity to reduce illumination energy consumption and enhance indoor comfort. Nevertheless, the training of such models requires a substantial quantity of data, and they are not yet scalable. Some studies [13; 14; 15] transformed the indoor lighting problem into a mathematical optimization problem using an intelligent optimization algorithm to find a globally optimal solution to the demand response problem in the lighting system in order to optimize the lighting system's energy consumption and achieve uniform lighting in the room.