BACKGROUND: Offline reinforcement learning (RL) has been increasingly applied to clinical decision-making problems. However, due to the lack of a standardized pipeline, prior work often relied on strategies that may lead to overfitted policies and in...
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-...
Reinforcement learning (RL) is a computational framework that models how agents learn from trial and error to make sequential decisions. Rooted in behavioural psychology, RL has become central to artificial intelligence and is increasingly applied in...
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical models of perception and action. This effort is driven by a multidiscip...
We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this appro...
Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor skills). The latter involves full-body engagement and ...
Robotic manipulation remains one of the most difficult challenges in robotics, with approaches ranging from classical model-based control to modern imitation learning. Although these methods have enabled substantial progress, they often require exten...
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 ...
Recognition of conspecific individuals in mammals is an important skill, thought to be mediated by a distributed array of neural networks, including those processing olfactory cues. Recent data from our groups have shown that social memory can be sup...
This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learning with explainable artificial intelligence. The platform addresses the challenges of personalized tr...
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