Ⅰ. 서론 
Ⅱ. 싱글 에이전트 강화학습
Ⅲ. 멀티 에이전트 강화학습
IV. 분산 강화학습 프레임워크
V. 가상 학습 환경 
Ⅵ. 응용 분야
Ⅶ. 결론
약어 정리

초록

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.

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