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%20represents%20a%20transformative%20advance%20for%20simulating%20and%20optimizing%20complex%20systems%20in%20a%20controlled%20digital%20space.%20Despite%20their%20potential%2C%20the%20challenge%20of%20constructing%20DTs%20that%20accurately%20replicate%20and%20predict%20the%20dynamics%20of%20real-world%20systems%20remains%20substantial.%20This%20paper%20introduces%20an%20intelligent%20framework%20for%20the%20construction%20and%20evaluation%20of%20DTs%2C%20specifically%20designed%20to%20enhance%20the%20accuracy%20and%20utility%20of%20DTs%20in%20testing%20algorithmic%20performance.%20We%20propose%20a%20novel%20construction%20methodology%20that%20integrates%20deep%20learning-based%20policy%20gradient%20techniques%20to%20dynamically%20tune%20the%20DT%20parameters%2C%20ensuring%20high%20fidelity%20in%20the%20digital%20replication%20of%20physical%20systems.%20Moreover%2C%20the%20Mean%20STate%20Error%20(MSTE)%20is%20proposed%20as%20a%20robust%20metric%20for%20evaluating%20the%20performance%20of%20algorithms%20within%20these%20digital%20space.%20The%20efficacy%20of%20our%20framework%20is%20demonstrated%20through%20extensive%20simulations%20that%20show%20our%20DT%20not%20only%20accurately%20mirrors%20the%20physical%20reality%20but%20also%20provides%20a%20reliable%20platform%20for%20algorithm%20evaluation.%20This%20work%20lays%20a%20foundation%20for%20future%20research%20into%20DT%20technologies%2C%20highlighting%20pathways%20for%20both%20theoretical%20enhancements%20and%20practical%20implementations%20in%20various%20industries.)
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