期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2018
卷号：12
期号：3
DOI：10.14313/JAMRIS_3-2018/15
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL) in the 90s, especially, the breakthrough of convolutional networks in computer vision field. The adoption of DL neural networks in RL, in the first decade of the 21 century, led to an end-to- end framework allowing a great advance in human-level agents and autonomous systems, called deep reinforce- ment learning (DRL). In this paper, we will go through the development Timeline of RL and DL technologies, describ- ing the main improvements made in both fields. Then, we will dive into DRL and have an overview of the state-of- the-art of this new and promising field, by browsing a set of algorithms (Value optimization, Policy optimization and Actor-Critic), then, giving an outline of current chal- lenges and real-world applications, along with the hard- ware and frameworks used. In the end, we will discuss some potential research directions in the field of deep RL, for which we have great expectations that will lead to a real human level of intelligence.
关键词：reinforcement learning; deep learning; ; convolutional network; recurrent network; deep ; reinforcement learning
