# A Modified Quad Q Network Algorithm for Predicting Resource Management
## 1 Introduction  
Enterprise production systems are attracting serious concern across the economy due to their negative impacts on the industrial environment and market change. The global economy is experiencing an unprecedented demand for resources to seek for new investments with reinforcement and expansion of smart factory infrastructures and large adoption of renewable industrial resources [1]. The global economy is experiencing an unprecedented demand for resource seeking new investments for the reinforcement and expansion of smart factory infrastructures and the large adoption of renewable infrastructures and the large adoption of renewable enterprise resources [2].  
As a result, companies around the world are experiencing continuous global restructuring, enacting basic rules and laws for development direction and business expansion. A.I. programs are being introduced in some of the smart factories as resource options for reducing the supply of enterprise during certain periods of time balance supply and demand [3].  
However, due to various factors, the number of data type parameters and the accuracy of artificial intelligence models are clearly limited for introducing them into thefield of practice [4]. Lack of quantity of datasets and errors in datasets as representative thresholds presents a serious obstacle to learning artificial intelligence. In this paper, we chose reinforcement learning as a solution to the problem of deficient datasets. The reward system for reinforcement learning varies from one learning system to another by weight [5]. We used an applicable DQN for fuzzy logic in this paper to demonstrate the uncertainty of reward schemes. Although the fuzzy logic was characterized by high levels of uncertainty, complexity, and nonlinearity [6], it was able to solve real-world uncertainty by simulating human experience in the form of rules [7]. Fuzzy logic was introduced by [PERSON]. It is a mathematical discipline used to express human reasoning in terms of rigorous mathematical notations. Human beings can make decisions even in the presence of imprecise and incomplete knowledge. Fuzzy logic can facilitate the approximation of human reasoning, beginning with a set of user-supplied human actions. It then builds rules [8]. The fuzzy system can convert these rules into their mathematical equivalents. Additional benefits of the fuzzy logic include its simplicity and its flexibility that can aid in solving problems consisting of imprecise and incomplete data. It can also be used to model nonlinear functions of arbitrary complexities [9].  
The potential of fuzzy logic has been explored in numerous fields of artificial intelligence, including signal processing, speech recognition, aerospace, robotics, embedded controllers, networking, business, and marketing. Moreover, fuzzy logic in reinforcement learning has been shown to be a promising technique due to its ability to efficiently facilitate the combination and evaluation of various parameters [10].  
However, the reinforcement learning theory was mainly directed at improving the accuracy of the reward value, and there was insufficient evidence to minimize the loss value. The concern with these loss values is often associated with Overfitting issues, which creates further issues. In this paper, we find a solution to the problem when deriving the Loss value from the Double Dueling DQN algorithm. The output of the proposed Quad Q Network algorithm is derived from the Compare Optimizer task, which reduces the loss value. We also demonstrate our performance experimentally using two Sample Data, Total Resource on Google and Apple, and mathematical proofs of the derivation of Double Q value.  
The rest of the paper is presented as follows. Section 2 describes previous researches in this field. Section 3 discusses the fundamentals of Dueling Deep Q Network and Double Dueling Deep Q Network as well as the proposed Quad Q Network. Section 4 presents the proposed experiment and results. Section 5 presents a summary of this work and future directions.