## 4 Conclusions  
Natural light is important for well-being. It not only improves productivity but reduces energy bills. So the objective of this study is to maximize the usage of available daylight. Though natural light has many advantages, inefficient control of it can lead to  
Figure 19: Rewards achieved by the DQN agent at different time steps over the course of year. Figure 18: Illuminance values obtained with the DQN agent suggested combinations over a year. visual discomfort to the occupants. So intelligent control natural light coming through fenestration system is important. Although the primary reason to install a shading system is to prevent glare. Apart from glare it also helps in preventing overheating due to high-intensity solar radiation. It not only reduces lighting load but can also reduce the high cooling load in summers and heating load in winters, thus saving energy costs. It improves overall comfort by allowing us to obtain the benefits of natural light at the same time. A shading system such as Electrochromic glass has some extra advantages like being able to control the intensity of its opaqueness from being almost dark to completely transparent and also the individual zones thus giving more flexibility in allowing the sunlight into workspaces. Electrochromic glass also appears aesthetically pleasing when compared to regular blinds thus improving the mood. In our study, the Electrochromic glass can be switched to four states with varying transmittance. In the clearest state, it transmits about 68% of the incident visible light, whereas in the darkest state it transmits only 1% of the visible light. Since the Electrochromic glass can be divided into zones and each zone can be switched to different levels of transmittance, we can adjust these zones in such a way that we obtain only the required amount of daylight. The goal of this research is to choose the right combination w.r.t the current sky condition such that glare and bright light can be avoided. In addition, keeping in context here is the viewpoint of the observer, as when there is more than one observer in the room, i.e., several viewpoints, then the feedback from all the observers are to be considered for effective control. Since the automation of the electrochromic glass needs a feedback loop to learn, reinforcement learning suits very well in this context. This is because we can formulate a specific reward function that suits the problem. In our problem, the reinforcement learning agent first observes the environment with the help of sun position, solar radiation, and workplace occupancy and tries to take any action, by switching to one of the 64 combinations of Electrochromic glass. As a result, the state of the environment changes, either improving visual comfort or making it worse for the occupant. The control parameters for visual discomfort are glare and bright daylight, which can be quantified with metrics like DGP and vertical eye illuminance. A simulated environment of fisheye HDR images is set up with the help of Radiance and evaluated using Evalglare. The evaluated values are used to reward or penalize the agent. A complex reward function that suits this problem is derived based on several trials and the agent's behaviour. Training the RL models is performed with the data from the weather file for the German city of Mannheim. A neural network is chosen as the learning model to accommodate the huge number of observations, states, and action combinations. Since training involves conducting several experiments in parallel and also optimal usage of resources asynchronyend tuning algorithm is used to tune the experiments. The results are evaluated separately based on the availability of useful daylight and glare reduction. In both cases, the DQN agent performed better than the benchmarks taken. * In the case of bright daylight hours, it performs as well as its benchmark combination 330, whereas in the useful daylight scenario it does better than its benchmark combination 000 allowing up to 90% daylight.