Skill-based reinforcement learning has become the mainstream approach to solve sparse-rewards decision making tasks. The skills extracted from the demonstration datasets provide the temporal abstraction. However, in previous skill-based RL methods, the skills are kept fixed during online learning, which brings in sub-optimal asymptotic performance when the dataset contains only sub-optimal behavior modes.

With the growing use of multiple social platforms, aligning user identities across networks, known as Social Network Alignment (SNA), has become crucial for personalized services, fraud detection, and online safety. However, existing SNA methods face significant challenges when dealing with sparse connections, heterogeneous structures, and dynamic changes in real-world social networks.

Artificial intelligence is beginning to transform not only drug discovery but also the design of advanced biomaterials. In a new study, researchers combined machine learning with laboratory validation to develop a data-driven strategy for identifying bioactive nucleoside hydrogels for periodontitis treatment. By integrating predictive modeling with innovative molecular scoring methods, the work aims to streamline biomaterial discovery and accelerate the development of targeted therapies for oral health applications.