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장수영,윤현진,박노삼,윤재관,손영성,Jang, S.Y.,Yoon, H.J.,Park, N.S.,Yun, J.K.,Son, Y.S. 한국전자통신연구원 2019 전자통신동향분석 Vol.34 No.4
Recent trends in deep reinforcement learning (DRL) have revealed the considerable improvements to DRL algorithms in terms of performance, learning stability, and computational efficiency. DRL also enables the scenarios that it covers (e.g., partial observability; cooperation, competition, coexistence, and communications among multiple agents; multi-task; decentralized intelligence) to be vastly expanded. These features have cultivated multi-agent reinforcement learning research. DRL is also expanding its applications from robotics to natural language processing and computer vision into a wide array of fields such as finance, healthcare, chemistry, and even art. In this report, we briefly summarize various DRL techniques and research directions.