{"title":"Enhanced Dyna-QPC model with Fuzzy logic to train gaming models","authors":"H. Ignatious, H. El-Sayed, Manzoor Khan","doi":"10.1109/gcaiot53516.2021.9692963","DOIUrl":null,"url":null,"abstract":"This paper presents an automated learning process to train the mountain car game model. It proposes an Enhanced Dyna-QPC model to effectively train the mountain car model in the stipulated time, based on their perceived environmental conditions. Decision Tree (DT) classification model along with Neural Network (NN)) model is used in this research to frame decision rules and self-train the game model respectively. Discrete Finite Deterministic Automata (DFA) concepts are included to finalize the state transition of the training model. Moreover, the Erdos-Renyi Random graph-generating model is used to generate dynamic state transition graphs to minimize the number of states. To increase the range of conditions and to derive meaningful decision rules, fuzzy concepts are used in this paper. Various simulation experiments have been conducted to evaluate the efficiency of the proposed training process. Simulation results reveal better performance over 3 popular models in the literature.","PeriodicalId":169247,"journal":{"name":"2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/gcaiot53516.2021.9692963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents an automated learning process to train the mountain car game model. It proposes an Enhanced Dyna-QPC model to effectively train the mountain car model in the stipulated time, based on their perceived environmental conditions. Decision Tree (DT) classification model along with Neural Network (NN)) model is used in this research to frame decision rules and self-train the game model respectively. Discrete Finite Deterministic Automata (DFA) concepts are included to finalize the state transition of the training model. Moreover, the Erdos-Renyi Random graph-generating model is used to generate dynamic state transition graphs to minimize the number of states. To increase the range of conditions and to derive meaningful decision rules, fuzzy concepts are used in this paper. Various simulation experiments have been conducted to evaluate the efficiency of the proposed training process. Simulation results reveal better performance over 3 popular models in the literature.