AIMC Topic: Reinforcement, Psychology

Clear Filters Showing 1 to 10 of 274 articles

Data-driven equation discovery reveals nonlinear reinforcement learning in humans.

Proceedings of the National Academy of Sciences of the United States of America
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 ...

Reinforced Odor Representations in the Anterior Olfactory Nucleus Can Serve as Memory Traces for Conspecifics.

eNeuro
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...

Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment.

Scientific reports
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...

The design of consumer behavior prediction and optimization model by integrating DQN and LSTM.

PloS one
Amidst the rapid evolution of e-commerce and the growing abundance of consumer shopping data, accurately identifying consumer interests and enabling targeted outreach has become a critical focus for merchants and researchers. This study introduces th...

Integrated decision-control for social robot autonomous navigation considering nonlinear dynamics model.

PloS one
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...

Encoding flexible gait strategies in stick insects through data-driven inverse reinforcement learning.

Bioinspiration & biomimetics
Stick insects exhibit remarkable adaptive walking capabilities across diverse environments; however, the mechanisms underlying their gait transitions remain poorly understood. Although reinforcement learning (RL) has been employed to generate insect-...

Egocentric value maps of the near-body environment.

Nature neuroscience
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...

Multi-agent self-attention reinforcement learning for multi-USV hunting target.

Neural networks : the official journal of the International Neural Network Society
A reinforcement learning (RL) method based on the multi-head self-attention (MSA) mechanism is proposed to solve the challenge of multiple unmanned surface vehicles (multi-USV) hunting target at the surface. The kinematic, dynamic, and environmental ...

Reinforcement Learning-Driven Path Generation for Ankle Rehabilitation Robot Using Musculoskeletal-Informed Energy Optimization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In rehabilitation robotics, optimizing energy consumption and high interaction forces is essential to prevent unnecessary muscle fatigue and excessive joint loading as they often cause an inefficient trajectory planning and disrupt natural movement p...

Learn to explain transformer via interpretation path by reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
In recent years, the Transformer model has become a key part of many AI systems, making it important to understand how it works. The large parameter size and complex structure of the Transformer make interpretation more difficult and less efficient. ...