The application of path planning algorithm based on deep reinforcement learning for mobile robots

Siyi Tian, Shuo Lei, Qiming Huang, Anyi Huang
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引用次数: 1

Abstract

To meet the need for autonomous route planning for tour guide robots in tourist venues, this paper proposes a path planning algorithm based on deep reinforcement learning. The traditional Deep Q-learning Network (DQN) algorithm two defects - overfitting and overestimation. This paper adopts a method that discards the experience pool and treats behavioural values equally, which not only solves the shortcomings of the traditional method, but also satisfies the need for mobile robots to lead tourists on tours through autonomous learning. The paper analyses the principle and process of the method and compares it with the traditional method through experiments to verify that the method outperforms the traditional method in terms of accuracy and speed.
基于深度强化学习的路径规划算法在移动机器人中的应用
为满足旅游场所导游员机器人自主路径规划的需求,提出了一种基于深度强化学习的路径规划算法。传统深度q -学习网络(DQN)算法存在过拟合和过估计两大缺陷。本文采用抛弃经验池,平等对待行为价值的方法,既解决了传统方法的不足,又通过自主学习满足了移动机器人带领游客游览的需求。本文分析了该方法的原理和过程,并通过实验与传统方法进行了比较,验证了该方法在精度和速度上都优于传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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