전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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멀티에이전트 강화학습 기술 동향: 분산형 훈련-분산형 실행 프레임워크를 중심으로

Survey on Recent Advances in Multiagent Reinforcement Learning Focusing on Decentralized Training with Decentralized Execution Framework

  • 신영환 (정보전략부 ) ;
  • 서승우 (정보전략부 ) ;
  • 유병현 (복합지능연구실 ) ;
  • 김현우 (복합지능연구실 ) ;
  • 송화전 (복합지능연구실 ) ;
  • 이성원 (정보전략부 )
  • Y.H. Shin ;
  • S.W. Seo ;
  • B.H. Yoo ;
  • H.W. Kim ;
  • H.J. Song ;
  • S. Yi
  • 발행 : 2023.08.01
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초록

The importance of the decentralized training with decentralized execution (DTDE) framework is well-known in the study of multiagent reinforcement learning. In many real-world environments, agents cannot share information. Hence, they must be trained in a decentralized manner. However, the DTDE framework has been less studied than the centralized training with decentralized execution framework. One of the main reasons is that many problems arise when training agents in a decentralized manner. For example, DTDE algorithms are often computationally demanding or can encounter problems with non-stationarity. Another reason is the lack of simulation environments that can properly handle the DTDE framework. We discuss current research trends in the DTDE framework.

키워드

과제정보

본 연구는 한국전자통신연구원 내부연구과제의 일환으로 수행되었음[멀티에이전트 강화학습 탐색, 통신, 학습전략 기술 연구, 22YE1210, 자율성장형 복합인공지능 원천기술 연구, 23ZS1100].

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