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SDN에서 강화학습을 통한 효과적인 멀티캐스트 라우팅 트리 생성 방법
채지훈(Jihun Chae),이병대(Byoung-Dai Lee),김남기(Namgi Kim) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.10
Along with the development of artificial intelligence technology, researches that apply reinforcement learning to routing problems in the network field are emerging. However, the basic reinforcement learning method assumes a fixed environment, so performance is limited in variable network environment that varies over time. Therefore, we proposes a deep reinforcement learning-based multicast routing tree construction method that can overcome these limitations and reflect the variable network environment in SDN. To evaluate the method proposed, experiments were performed to compare performance in various network topology. As a result, It was found that the deep reinforcement learning agent learned by proposed method in various network topology produced optimal close multicast routing tree than deep reinforcement learning agent learned in fixed network topology.