Robot reinforcement learning accuracy-based learning classifier systems with Fuzzy Policy Gradient descent(XCS-FPGRL)

被引:0
|
作者
Shao, Jie [1 ]
Yu, Jingru [1 ]
机构
[1] Zhengzhou Chenggong Univ Finance & Econ, Dept Informat Engn, Zhengzhou 451200, Peoples R China
来源
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCES IN MECHANICAL ENGINEERING AND INDUSTRIAL INFORMATICS | 2015年 / 15卷
关键词
Convergence; Rrobot; Reinforcement learning; Accuracy-based learning classifier system with Gradient descent (XCS-FPGRL); XCS (Accuracy-based learning classifier system);
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presented a novel approach XCS-FPGRL to research on robot reinforcement learning. XCS-FPGRL combines covering operator and genetic algorithm. The systems is responsible for adjusting precision and reducing search space according to some reward obtained from the environment, acts as an innovation discovery component which is responsible for discovering new better reinforcement learning rules. The experiment and simulation showed that robot reinforcement learning can achieved convergence very quickly.
引用
收藏
页码:1013 / 1018
页数:6
相关论文
共 50 条
  • [21] Fuzzy Policy Reinforcement Learning in Cooperative Multi-robot Systems
    Dongbing Gu
    Erfu Yang
    Journal of Intelligent and Robotic Systems, 2007, 48 : 7 - 22
  • [22] Fuzzy policy reinforcement learning in cooperative multi-robot systems
    Gu, Dongbing
    Yang, Erfu
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2007, 48 (01) : 7 - 22
  • [23] X-TCS: Accuracy-based learning classifier system robotics
    Studley, M
    Bull, L
    2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, 2005, : 2099 - 2106
  • [24] Application of Accuracy-based Learning Classifier System in Human Resource Management
    Sun, Jin-Li
    2015 INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND INFORMATION SYSTEM (SEIS 2015), 2015, : 623 - 628
  • [25] An Investigation of Real-Valued Accuracy-Based Learning Classifier Systems for Electronic Fraud Detection
    Behdad, Mohammad
    Barone, Luigi
    French, Tim
    Bennamoun, Mohammed
    GECCO-2010 COMPANION PUBLICATION: PROCEEDINGS OF THE 12TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2010, : 1893 - 1900
  • [26] Learning for hierarchical fuzzy systems based on the gradient-descent method
    Wang, Di
    Zeng, Xiao-Jun
    Keane, John A.
    2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2006, : 92 - +
  • [27] Reinforcement Learning in Continuous Spaces by Using Learning Fuzzy Classifier Systems
    Chen, Gang
    Douch, Colin
    Zhang, Mengjie
    Pang, Shaoning
    NEURAL INFORMATION PROCESSING, PT II, 2015, 9490 : 320 - 328
  • [28] Fuzzy Baselines to Stabilize Policy Gradient Reinforcement Learning
    Surita, Gabriela
    Lemos, Andre
    Gomide, Fernando
    EXPLAINABLE AI AND OTHER APPLICATIONS OF FUZZY TECHNIQUES, NAFIPS 2021, 2022, 258 : 436 - 446
  • [29] USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS
    Juan, Alejandro
    Pazzi, Richard W.
    Boukerche, Azzedine
    JOURNAL OF INTERCONNECTION NETWORKS, 2009, 10 (04) : 365 - 390
  • [30] Comparing Reinforcement Learning algorithms applied to crisp and fuzzy Learning Classifier Systems
    Bonarini, A
    GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 1999, : 52 - 59