Background and Research Objectives Reinforcement Learning (RL) has recently become a dynamic and transformative field within artificial intelligence, aiming to maximize cumulative rewards through the interaction between agents and the environment. However, the application of RL faces challenges in optimizing the Bellman Error. This error is particu...

With the continuous advancement of data acquisition technology, obtaining large amounts of high-dimensional data containing multiple features has become very easy, such as images and vision data. However, traditional machine learning methods, especially those based on vectors and matrices, face challenges such as the curse of dimensionality, increa...

Fast Synchronization Control and Application for Encryption-Decryption of Coupled Neural Networks With Intermittent Random Disturbance I. Background and Research Motivation In recent years, neural networks have been widely applied in various fields such as data classification, image recognition, and combinatorial optimization problems. Regarding th...

Research on 3D/2D Vascular Registration Based on Monte Carlo Tree Search and Manifold Regularization In interventional vascular surgery, enhanced intraoperative real-time imaging technology can compensate for the shortcomings of DSA navigation, such as the lack of depth information and excessive use of toxic contrast agents, by projecting preoperat...

A Framework for Solving Graph Optimization Problems Based on Graph Neural Networks Background and Research Motivation In solving Constraint Satisfaction Problems (CSPs) and Combinatorial Optimization Problems (COPs), a common method is the combination of backtracking and branch heuristics. Although branch heuristics designed for specific problems a...