###### Abstract  
The semi-active control system is widely used to reduce the seismic response of building structures. Its control performance mainly depends on the applied control algorithms. Various semi-active control algorithms have been developed to date. Recently, machine learning has been applied to various engineering fields and provided successful results. Because reinforcement learning (RL) has shown good performance for real-time decision-making problems, structural control engineers have become interested in RL. In this study, RL was applied to the development of a semi-active control algorithm. Among various RL methods, a Deep Q-network (DQN) was selected because of its successful application to many control problems. A sample building structure was constructed by using a semi-active mid-story isolation system (SMIS) with a magnetorheological damper. Artificial ground motions were generated for numerical simulation. In this study, the sample building structure and seismic excitation were used to make the RL environment. The reward of RL was designed to reduce the peak story drift and the isolation story drift. Skyhook and groundhook control algorithms were applied for comparative study. Based on numerical results, this paper shows that the proposed control algorithm can effectively reduce the seismic responses of building structures with a SMIS. semi-active mid-story isolation system; control algorithm; reinforcement learning; seismic response reduction; magnetorheological damper; deep q-network +
Footnote †: journal: Applied Sciences  
fields. Reinforcement learning (RL) is one of the machine learning methods using an agent that interacts with the environment for learning. Reinforcement learning is a model-free framework for solving the control problem presented by a Markov decision process (MDP) [12]. Such RL has been applied to various types of active control problems, such as vehicles [13], controllable tensegrity structures [14], and robotics [15], presenting successful control performances. Recent advancements in reinforcement learning have attracted the attention of structural engineers. [PERSON] et al. [16] used a RL framework for an active control system (ACS) of a five-story building. The proposed method was successful in reducing dynamic responses in comparison with a conventional ACS. [PERSON] et al. applied reinforcement learning to the real-time control of an active mass driver system as a seismic control system [17]. In their work, a control method was developed for the ACS, considering structural uncertainties. Although several studies on the application of reinforcement learning to an ACS for seismic response reduction have been conducted, to the best of the authors' knowledge, application of reinforcement learning to a SACS for seismic protection of building structures has rarely been reported. Most of the studies on the application of reinforcement learning to SACS have focused on vehicle suspension systems [18; 19]. Based on this background, the authors developed a semi-active control algorithm for the seismic damage protection of building structures using reinforcement learning. From among various semi-active earthquake-resisting systems for building structures, a seismic isolation system was used in this study. While a base isolation system is successfully used for seismic response reduction of low- and mid-rise structures, a mid-story isolation system is applied to high-rise building structures [20; 21]. An existing building structure with a mid-story isolation system was selected as a sample structure (Shiodome Sumitomo building in Japan [20]). The nonlinear hysteretic dampers in the isolation system of the Shiodome Sumitomo building were replaced by magnetorheological (MR) dampers to compose a semi-active mid-story isolation system (SMIS). Artificial earthquakes were generated for numerical simulation. To optimize the control commands sent to the MR dampers in the SMIS, a deep Q-network (DQN) algorithm was selected in this study.