Combination of reinforcement learning and bee algorithm for controlling two-link arm with six muscle: simplified human arm model in the horizontal plane.
{"title":"Combination of reinforcement learning and bee algorithm for controlling two-link arm with six muscle: simplified human arm model in the horizontal plane.","authors":"Fereidoun Nowshiravan Rahatabad, Parisa Rangraz","doi":"10.1007/s13246-019-00828-4","DOIUrl":null,"url":null,"abstract":"<p><p>The aim of this study was to improve reinforcement learning algorithm by combining artificial bee colony algorithm. The traditional method of reinforcement learning algorithm has a very low convergence rate due to random choices. An ant algorithm will help to make random choices in reinforcement learning more appropriate. This hybrid algorithm called the bee colony reinforcement (BCR) algorithm. The tip of the arm must reach a predetermined purpose by BCR algorithm. The results show that the BCR algorithm in the model has been able to reduce the time to reach the goal than the reinforcement learning algorithm (In average 12 steps faster). Also, the path for reaching the goal in the BCR algorithm was far more direct and shorter than the reinforcement learning algorithm. This method also detects the optimal path towards the goal.</p>","PeriodicalId":55430,"journal":{"name":"Australasian Physical & Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australasian Physical & Engineering Sciences in Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13246-019-00828-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0
Abstract
The aim of this study was to improve reinforcement learning algorithm by combining artificial bee colony algorithm. The traditional method of reinforcement learning algorithm has a very low convergence rate due to random choices. An ant algorithm will help to make random choices in reinforcement learning more appropriate. This hybrid algorithm called the bee colony reinforcement (BCR) algorithm. The tip of the arm must reach a predetermined purpose by BCR algorithm. The results show that the BCR algorithm in the model has been able to reduce the time to reach the goal than the reinforcement learning algorithm (In average 12 steps faster). Also, the path for reaching the goal in the BCR algorithm was far more direct and shorter than the reinforcement learning algorithm. This method also detects the optimal path towards the goal.
期刊介绍：
Australasian Physical & Engineering Sciences in Medicine (APESM) is a multidisciplinary forum for information and research on the application of physics and engineering to medicine and human physiology. APESM covers a broad range of topics that include but is not limited to:
- Medical physics in radiotherapy
- Medical physics in diagnostic radiology
- Medical physics in nuclear medicine
- Mathematical modelling applied to medicine and human biology
- Clinical biomedical engineering
- Feature extraction, classification of EEG, ECG, EMG, EOG, and other biomedical signals;
- Medical imaging - contributions to new and improved methods;
- Modelling of physiological systems
- Image processing to extract information from images, e.g. fMRI, CT, etc.;
- Biomechanics, especially with applications to orthopaedics.
- Nanotechnology in medicine
APESM offers original reviews, scientific papers, scientific notes, technical papers, educational notes, book reviews and letters to the editor.
APESM is the journal of the Australasian College of Physical Scientists and Engineers in Medicine, and also the official journal of the College of Biomedical Engineers, Engineers Australia and the Asia-Oceania Federation of Organizations for Medical Physics.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
