Exploration of Unknown Environments Using Deep Reinforcement Learning

被引：0
|
作者：
McCalmon, Joseph [1 ]
机构：
[1] Wake Forest Univ, Dept Comp Sci, 1834 Wake Forest Rd, Winston Salem, NC 27109 USA
来源：
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2021年 / 35卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
My research presents a method for efficient exploration of an outdoor, unknown area, which aims to achieve precise coverage of regions of interest within that area. While this method for autonomous exploration was designed for autonomous controllers in unmanned aerial vehicles (UAVs), the concepts apply to any vehicle which uses autonomous navigation. We consider an environment with areas of interest of various sizes littered throughout, and a reinforcement learning agent which is tasked with discovering and mapping these areas in an efficient manner.
引用
收藏
页码：15970 / 15971
页数：2
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