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Detail
Journal Article
심층 강화 학습 기술 동향
- Issue Date
- 2019-08
- Citation
- 전자통신동향분석, v.34, no.4, pp.1-14
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Computational Efficiency, Computer Vision(CV), Deep reinforcement learning, Multiple Agents, Natural Language Processing, Partial observability, Recent Trends, Reinforcement Learning(RL), multi-agent reinforcement learning, multi-task
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
