한국공간구조학회논문집 (Journal of Korean Association for Spatial Structures)

  • 제22권2호
  • /
  • Pages.39-46
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  • 2022
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  • 1598-4095(pISSN)
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  • 2287-7401(eISSN)
한국공간구조학회 (Korean Association for Spatial Structures)
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스마트 제어알고리즘 개발을 위한 강화학습 리워드 설계

Reward Design of Reinforcement Learning for Development of Smart Control Algorithm

  • 김현수 (선문대학교 건축학부) ;
  • 윤기용 (선문대학교 건설시스템안전공학과)
  • Kim, Hyun-Su (Division of Architecture, Sunmoon University) ;
  • Yoon, Ki-Yong (Department of Civil Infrastructure Systems and Safety Engineering, Sunmoon University)
  • 투고 : 2022.05.12
  • 심사 : 2022.06.10
  • 발행 : 2022.06.15
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초록

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 hyper-parameters 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.

키워드

과제정보

본 논문은 2019년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임.(No. NRF-2019R1A2C1002385).

참고문헌

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