다중 에이전트 강화학습을 이용한 RC보 최적설계 기술개발

서지정보

ㆍ발행기관 : 한국공간구조학회 ㆍ수록지정보 : 한국공간구조학회지 / 23권 / 2호
ㆍ저자명 : 강주원, 김현수

목차

Abstract
1. 서론
2. RC 보 설계를 위한 강화학습 환경
3. 다중 에이전트 강화학습의 개요
4. 다중 에이전트 강화학습을 이용한RC보 최적 설계
5. 결론
감사의 글
References

영어 초록

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

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