使用深度强化学习的自主视觉导航:概述

M. Ejaz, T. Tang, Cheng-Kai Lu
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引用次数: 3

摘要

采用深度学习技术的强化学习(RL)算法有助于解决当今世界的许多复杂问题,例如使用原始图像作为输入来玩视频游戏和机器人自主导航。深度学习为强化学习提供了一种机制,使智能体能够解决人类级别的任务。强化学习的兴起始于一名计算机玩家在最困难的围棋游戏b[6]中击败了人类专家。在本文中,我们讨论了一些重要的主题,如强化学习的一般观点,强化学习的方法和算法,以及强化学习面临的挑战。最后,我们讨论了在机器人自主视觉导航领域中RL实现算法的概况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview
Reinforcement Learning (RL) algorithm with deep learning techniques helps to solve many complex problems of today's world, such as to play a video game and autonomous navigation in the robots using the raw image as an input. Deep learning provides the mechanism to RL which enables the agent to solve the human level task. The rise of RL begins when a computer player beat the human expert in the most difficult game Go [6]. In this paper, we discuss some important topics such as the general view of reinforcement learning, methods, and algorithms of reinforcement learning and challenges which reinforcement learning is facing. Finally, we discussed a survey of implemented algorithms of RL in the field of robotics for autonomous visual navigation.
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