Review of Research on Treatment of Lung Infections with Novel Coumarin Derivatives Background With the increasing antibiotic resistance, especially the problem of Acinetobacter baumannii’s resistance to antibiotics, researchers worldwide have begun to search for new antimicrobial agents. This Gram-negative bacterium, with its high survivability and...

Comparison of Experiences and Descriptions of Basic Economic Preferences in 11 Countries Background and Motivation Recent studies have shown that humans exhibit a high degree of context-dependency in encoding the value of rewards, which in some cases leads to suboptimal decisions. However, it remains unclear whether this computational limitation is...

Discussion on Risk-Sensitive Robot Control Based on Self-Attention Mechanism Research Background The kinematics and dynamics in robot control are key factors to ensure the precise completion of tasks. Most robot control schemes rely on various models to achieve task optimization, scheduling, and priority control. However, the dynamic characteristic...

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...

Real-World Humanoid Robot Walking Based on Reinforcement Learning Background Introduction Humanoid robots have enormous potential for autonomous operation in diverse environments, not only alleviating labor shortages in factories but also assisting elderly people at home and exploring new planets. Although classical controllers show excellent effec...

Background In recent years, Reinforcement Learning (RL) has demonstrated its effectiveness in solving various complex tasks. However, many real-world decision-making and control problems involve multiple conflicting objectives. The relative importance (preference) of these objectives often needs to be balanced against each other in different scenar...

Traditional representation learning methods usually design a fixed basis function architecture to achieve desired properties such as orthogonality and sparsity. In contrast, the idea of deep reinforcement learning is that the designer should not encode the properties of the representation, but instead let the data flow determine the properties of t...