Enhanced Dyna-QPC model with Fuzzy logic to train gaming models

H. Ignatious, H. El-Sayed, Manzoor Khan
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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.
基于模糊逻辑的改进Dyna-QPC模型训练博弈模型
本文提出了一种训练山地车博弈模型的自动学习过程。提出了一种增强型Dyna-QPC模型,根据山地车感知到的环境条件,在规定的时间内有效地训练山地车模型。本研究采用决策树(DT)分类模型和神经网络(NN)模型分别构建决策规则和自训练博弈模型。采用离散有限确定性自动机(DFA)概念来完成训练模型的状态转换。利用Erdos-Renyi随机图生成模型生成动态状态转移图,使状态数量最小化。为了增加条件的范围,并得到有意义的决策规则,本文引入了模糊概念。已经进行了各种模拟实验来评估所提出的训练过程的效率。仿真结果表明,该模型的性能优于文献中常用的3种模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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