基于智能地图方法的Q-Learning灾害管理系统

Nilatpal Chakrabarty, Ankita Debnath, Deepanwita Mallick, S. Banerjee
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引用次数: 1

摘要

人工智能(AI)是研究界探索的发人深省的领域之一。在这个领域,强化学习和计算机视觉技术正在成为关注的焦点。人工智能的研究重点是开发和分析算法,在最小的人为干预下执行智能行为。此外,从一些文献中可以观察到,在机器人领域也进行了大量的研究。本文首先尝试设计一个由改进的Q-learning算法触发的智能系统,该系统可以在任何未知的灾难环境中进行映射,并以熟练的方式执行。然后,结合机器人的概念,实时测试所提出系统的增强性能。在此基础上,研究了系统在有约束和无约束等不同环境下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Q-Learning Elicited Disaster Management System Using Intelligent Mapping Approach
Artificial Intelligence (AI) is one of the thought provoking areas of exploration within the research community. In this area, reinforcement learning and computer vision techniques are coming under the focus. Research in AI focuses on the development and analysis of algorithms that perform intelligent behavior with minimal human intervention. Moreover, from several literatures it has been observed that enormous investigations have also been performed in robotics. In this paper, initially an attempt has been made to design an intelligent system triggered by improved Q-learning algorithm which can map in any unknown disaster environments and perform in a proficient fashion. Thereafter, the concept of robotics has been amalgamated to test the enhanced performance of the proposed system in real time. Thereafter, the performance of the proposed system has also been studied under different environments like constrained and unconstrained.
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