强化学习
人工智能
计算机科学
深度学习
领域(数学)
光学(聚焦)
钢筋
机器学习
算法
心理学
数学
社会心理学
光学
物理
纯数学
作者
S. V. Ivanov,A. G. D’yakonov
出处
期刊:Cornell University - arXiv
日期:2019-01-01
被引量:26
标识
DOI:10.48550/arxiv.1906.10025
摘要
Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research. In this work latest DRL algorithms are reviewed with a focus on their theoretical justification, practical limitations and observed empirical properties.
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