# A Modified Quad Q Network Algorithm for Predicting Resource Management
## 5 Conclusions  
Fuzzy reasoning mimics human reasoning and tests random actions that agents might take as reward functions in reinforcement learning. However, there has been no works on additional tasks particularly in loss value calibration tasks in conventional reinforcement learning.  
Figure 14: Learning results by substituting the Quad Q Network algorithm based on the proposed Total Resource on Apple.  
Figure 15: Comparative analysis of the proposed Q Network reward for the value of Total Resource on Apple.  
learning. In this paper, loss value correction with Quad Q Network has are shown to achieve superior performance. The presented simulation scenarios demonstrate that the proposed Quad Q Network algorithm could induce smooth supply and demand of enterprise resources and prevent use of surplus resources and resource missing events. This paper proposes a supply-demand response algorithm to minimise enterprise resource and resource price by shifting load supply-demand, in response to enterprise resource price signal and consumer preferences, from peak periods when the resource price is high to off-peak demand when the resource price is low. In this study, an effective household resource management tool was developed using Q-learning to deal with dynamic resource prices and different resource consumption patterns without compromising the users' lifestyle or preferences. The proposed quad Q Network performed better than other techniques through renewal of _Loss_ value using _Target_\(Q\) value as a task for minimizing the loss value to predict enterprise resources. Quad Q Network algorithm was proven to have good performances of loss reduction through other datasets. These algorithms could be used for predicting resources in other fields such as predicting future electricity demand, predicting computer resources, and predicting network communication resources in the future.  
Conceptualization, Y.K. and J.K. ([PERSON]); methodology, Y.K. and J.P.; software, Y.K. and J.P.; validation, J.P. and S.L.; formal analysis, J.P.; investigation, J.Y. and S.L.; resources, J.Y. and J.K. ([PERSON]); data curation, J.P. and S.L.; writing--original draft preparation, Y.K.; writing--review and editing, J.K. ([PERSON]) and S.L.; visualization, J.Y.; supervision, J.K. ([PERSON]) and S.L.; project administration, J.K. ([PERSON]); funding acquisition, J.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00314) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation) also the results of a study on the supported by the BK21 FOUR Program(Fostering Outstanding Universities for Research, 5199991714138) funded by the Ministry of Education(MOE, Korea) and National Research Foundation of Korea(NRF) and supported by the IITP grant funded by the MSIT(No. 2019-0-01343).  
Data Availability Statement: Data sharing not applicable.  
The authors declare that there are no conflicts of interest.