Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyperparameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

Abstract
1. 서론
2. 예제구조물 및 수치해석 기법
3. 강화학습 구성 및 리워드 설계
4. 스마트 제어알고리즘의 성능 검토
5. 결론
References

• 김현수(선문대학교 건축학부 교수) | Kim Hyun-Su (Division of Architecture, Sunmoon University)
• 윤기용(선문대학교 건설시스템안전공학과 교수) | Yoon Ki-Yong (Department of Civil Infrastructure Systems and Safety Engineering, Sunmoon University) Corresponding author