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...
Biomedical physics & engineering express
Dec 10, 2025
Major Depressive Disorder (MDD) diagnosis through Electroencephalography (EEG) is hindered by the non-stationary characteristics of neural oscillations and the limited adaptability of conventional classification frameworks. Static ensemble models, wh...
In the field of robotics control, prevailing research is progressively leveraging more sophisticated deep learning networks to enhance learning outcomes in specific domains of robotics motion control. Notably, the Decision Transformer (DT), a promine...
The Journal of neuroscience : the official journal of the Society for Neuroscience
Oct 8, 2025
In temporal lobe epilepsy, interictal spikes (IS)-hyper-synchronous bursts of network activity-occur at high rates in between seizures. We sought to understand the influence of IS on working memory by recording hippocampal local field potentials from...
Representation of external and internal states in the brain plays a critical role in enabling suitable behavior. Recent studies suggest that state representation and state value can be simultaneously learned through Temporal-Difference-Reinforcement-...
In probabilistic reversal learning, the choice option yielding reward with higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occ...
Proceedings of the National Academy of Sciences of the United States of America
Jul 31, 2025
Computational models of reinforcement learning (RL) have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, potentially ...
BACKGROUND: Anhedonia, a transdiagnostic feature common to both Major Depressive Disorder (MDD) and Schizophrenia (SCZ), is characterized by abnormalities in hedonic experience. Previous studies have used machine learning (ML) algorithms without focu...
Reinforcement learning (RL) has demonstrated significant potential in social robot autonomous navigation, yet existing research lacks in-depth discussion on the feasibility of navigation strategies. Therefore, this paper proposes an Integrated Decisi...
Body-part-centered response fields are pervasive in single neurons, functional magnetic resonance imaging, electroencephalography and behavior, but there is no unifying formal explanation of their origins and role. In the present study, we used reinf...
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