Determination of the optimal values of parameters in reinforcement learning for mobile robot navigation by a genetic algorithm

被引：1
|
作者：
Kamei, K [1 ]
Ishikawa, M [1 ]
机构：
[1] Kyushu Inst Technol, Grad Sch Life Sci & Syst Engn, Dept Brain Sci & Engn, Kitakyushu, Fukuoka 8080916, Japan
来源：
BRAIN-INSPIRED IT I | 2004年 / 1269卷
关键词：
genetic algorithm; reinforcement learning; mobile robot; navigation;
D O I：
10.1016/j.ics.2004.05.133
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Reinforcement learning is useful in location estimation, obstacle avoidance and path planning for autonomous mobile robots. We have to determine various parameters in reinforcement learning without prior information. We propose to determine the optimal values of parameters with the help of a genetic algorithm. We have succeeded in accelerating the speed of learning by about 20-30% and in decreasing the number of actions needed to reach the goal by about a half. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：193 / 196
页数：4
相关论文
共 50 条
  • [1] A genetic approach to optimizing the values of parameters in reinforcement learning for navigation of a mobile robot
    Kamei, K
    Ishikawa, M
    [J]. NEURAL INFORMATION PROCESSING, 2004, 3316 : 1148 - 1153
  • [2] Fuzzy Genetic Network Programming with Reinforcement Learning for Mobile Robot Navigation
    Sendari, Siti
    Mabu, Shingo
    Hirasawa, Kotaro
    [J]. 2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2011, : 2243 - 2248
  • [3] Deep Reinforcement Learning for Mobile Robot Navigation
    Gromniak, Martin
    Stenzel, Jonas
    [J]. 2019 4TH ASIA-PACIFIC CONFERENCE ON INTELLIGENT ROBOT SYSTEMS (ACIRS 2019), 2019, : 68 - 73
  • [4] Intellectual Method of guiding Mobile Robot Navigation using Reinforcement Learning Algorithm
    Nirmala, G.
    Geetha, S.
    Selvakumar, S.
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON ENGINEERING AND TECHNOLOGY (ICETECH), 2015, : 189 - 192
  • [5] Reinforcement Extreme Learning Machine for Mobile Robot Navigation
    Geng, Hongjie
    Liu, Huaping
    Wang, Bowen
    Sun, Fuchun
    [J]. PROCEEDINGS OF ELM-2016, 2018, 9 : 61 - 73
  • [6] Reinforcement learning based on backpropagation for mobile robot navigation
    Jaksa, R
    Majerník, P
    Sincák, P
    [J]. COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - NEURAL NETWORKS & ADVANCED CONTROL STRATEGIES, 1999, 54 : 46 - 51
  • [7] Mobile Robot Navigation Using Deep Reinforcement Learning
    Lee, Min-Fan Ricky
    Yusuf, Sharfiden Hassen
    [J]. PROCESSES, 2022, 10 (12)
  • [8] Mobile Robot Navigation based on Deep Reinforcement Learning
    Ruan, Xiaogang
    Ren, Dingqi
    Zhu, Xiaoqing
    Huang, Jing
    [J]. PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 6174 - 6178
  • [9] Reinforcement Learning Based Approach For Mobile Robot Navigation
    Jaseem, Mohammed M.
    Mathew, Robins
    Hiremath, Somashekhar S.
    [J]. PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND KNOWLEDGE ECONOMY (ICCIKE' 2019), 2019, : 524 - 527
  • [10] Reinforcement learning-based mobile robot navigation
    Altuntas, Nihal
    Imal, Erkan
    Emanet, Nahit
    Ozturk, Ceyda Nur
    [J]. TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2016, 24 (03) : 1747 - 1767
12345