Traditional asset allocation rules, while effective in stable phases, tend to erode once markets enter volatile regimes or undergo structural breaks. Research in deep reinforcement learning (DRL) has usually emphasized raw-return rewards, leaving asi...
Journal of neuroengineering and rehabilitation
Dec 19, 2025
BACKGROUND: Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the "right intervention, at the ri...
Proceedings of the National Academy of Sciences of the United States of America
Dec 12, 2025
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision-making domain, particularly in model-based reinforcement learning, re...
Constructing a universal moral code for artificial intelligence (AI) is challenging because human cultures have different values, norms, and social practices. We therefore argue that AI systems should adapt to culture based on observation: Just as a ...
Humans have to respond to everyday tasks with goal-directed actions in complex and time-continuous environments. However, modeling human behavior in such environments has been challenging. Deep Q-networks (DQNs), an application of deep learning used ...
With the continuous advancement of network transmission technology, more and more applications are being applied in wireless network environments, especially in places that require high coverage, such as oceans and mountainous areas. However, wireles...
Biologically-inspired jumping robots have demonstrated remarkable adaptability in complex environments, making them increasingly valuable across various fields. However, effective path planning in obstacle-dense environments for large-scale jumping r...
Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various...
Achieving robust and energy-efficient navigation in unknown fluid environments remains a key challenge for bioinspired underwater robots. In this study, we develop a reinforcement learning-based control framework that enables a fish-like swimmer to a...
Brain-machine interface (BMI) research has shown the efficacy of using motor and sensory-related neural signals to assist physically impaired patients. Despite the comparable ability to extract more abstract cognitive signals from the brain, little e...
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