Neural Q-learning in Motion Planning for Mobile Robot

被引：1
|
作者：
Qin, Zheng [1 ]
Gu, Jason [1 ]
机构：
[1] Dalhousie Univ, Dept Elect & Comp Engn, Halifax, NS B3J 2X4, Canada
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS ( ICAL 2009), VOLS 1-3 | 2009年
关键词：
Reinforcement learning; neural network; mobile robot; motion planning;
D O I：
10.1109/ICAL.2009.5262570
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to solve the bad convergence property of neural network which is used to generalize reinforcement learning, the neural and case based Q-learning (NCQL) algorithm is proposed. The basic principle of NCQL is that the reinforcement learning is generalized by NN, and the convergence property and learning efficiency are promoted by cases. The detail elements of the learning algorithm are fulfilled according to the application of motion planning for mobile robot. The simulation results show the validility and practicability of the NCQL algorithm.
引用
收藏
页码：1024 / 1028
页数：5
相关论文
共 50 条
  • [21] Path planning of mobile robots with Q-learning
    Cetin, Halil
    Durdu, Akif
    2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 2162 - 2165
  • [22] Path planning for autonomous mobile robot using transfer learning-based Q-learning
    Wu, Shengshuai
    Hu, Jinwen
    Zhao, Chunhui
    Pan, Quan
    PROCEEDINGS OF 2020 3RD INTERNATIONAL CONFERENCE ON UNMANNED SYSTEMS (ICUS), 2020, : 88 - 93
  • [23] Modified Q-learning with distance metric and virtual target on path planning of mobile robot
    Low, Ee Soong
    Ong, Pauline
    Low, Cheng Yee
    Omar, Rosli
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 199
  • [24] Path planning for mobile robot based on improved ant colony Q-learning algorithm
    Cui, Mengru
    He, Maowei
    Chen, Hanning
    Liu, Kunpeng
    Hu, Yabao
    Zheng, Chen
    Wang, Xuliang
    INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM, 2025, 19 (04): : 3069 - 3087
  • [25] Path planning of a mobile robot in a free-space environment using Q-learning
    Jianxun Jiang
    Jianbin Xin
    Progress in Artificial Intelligence, 2019, 8 : 133 - 142
  • [26] Path planning of a mobile robot in a free-space environment using Q-learning
    Jiang, Jianxun
    Xin, Jianbin
    PROGRESS IN ARTIFICIAL INTELLIGENCE, 2019, 8 (01) : 133 - 142
  • [27] Mobile robot Navigation Based on Q-Learning Technique
    Khriji, Lazhar
    Touati, Farid
    Benhmed, Kamel
    Al-Yahmedi, Amur
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2011, 8 (01): : 45 - 51
  • [28] Autonomous Exploration for Mobile Robot using Q-learning
    Liu, Yang
    Liu, Huaping
    Wang, Bowen
    2017 2ND INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM), 2017, : 614 - 619
  • [29] Q-Learning for Autonomous Mobile Robot Obstacle Avoidance
    Ribeiro, Tiago
    Goncalves, Fernando
    Garcia, Ines
    Lopes, Gil
    Fernando Ribeiro, A.
    2019 19TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC 2019), 2019, : 243 - 249
  • [30] Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot
    Zhang, Meiyan
    Cai, Wenyu
    Pang, Lingfeng
    IEEE ACCESS, 2023, 11 : 29673 - 29683
12345