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Reinforcement, Psychology

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Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Annals of the New York Academy of Sciences 40159374
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....
Algorithms Brain Brain-Computer Interfaces Deep Learning Electroencephalography Humans Reinforcement Machine Learning Reinforcement, Psychology
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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 40315091
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...
Adult Algorithms Ankle Ankle Joint Biomechanical Phenomena Electromyography Exoskeleton Device Female Humans Machine Learning Male Middle Aged Movement Muscle, Skeletal Reinforcement, Psychology Reproducibility of Results Robotics Stroke Rehabilitation Torque
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Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture.

Accident; analysis and prevention 40121971
The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is generating critical testing scenarios to accurately evaluate vehicle performance. To overcome the limita...
Accidents, Traffic Artificial Intelligence Automobile Driving Automobiles Humans Reinforcement, Psychology
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Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem.

Nature communications 40121193
A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in...
Cooperative Behavior Deep Learning Game Theory Humans Neural Networks, Computer Reinforcement Machine Learning Reinforcement, Psychology Resource Allocation Trust
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Capsule DenseNet++: Enhanced autism detection framework with deep learning and reinforcement learning-based lifestyle recommendation.

Computers in biology and medicine 40120178
Autism Spectrum Disorder (ASD) is a complex neurological condition that impairs the ability to interact, communicate, and behave. It is becoming increasingly prevalent worldwide, with an increase in the number of young children diagnosed with ASD in ...
Algorithms Autism Spectrum Disorder Autistic Disorder Child Child, Preschool Convolutional Neural Networks Deep Learning Female Humans Life Style Male Reinforcement, Psychology Saudi Arabia
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Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

PLoS computational biology 40096178
Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, wit...
Algorithms Animals Cerebellum Computational Biology Male Mice Mice, Inbred C57BL Models, Neurological Purkinje Cells Reinforcement, Psychology Reward Supervised Machine Learning
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Rethinking exploration-exploitation trade-off in reinforcement learning via cognitive consistency.

Neural networks : the official journal of the International Neural Network Society 40090299
The exploration-exploitation dilemma is one of the fundamental challenges in deep reinforcement learning (RL). Agents must strike a trade-off between making decisions based on current beliefs or gathering more information. Prior work mostly prefers d...
Cognition Decision Making Deep Learning Humans Neural Networks, Computer Reinforcement, Psychology Reward
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Mastering diverse control tasks through world models.

Nature 40175544
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to w...
Algorithms Artificial Intelligence Humans Learning Problem Solving Reinforcement, Psychology Task Performance and Analysis
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Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning.

Journal of neuroengineering and rehabilitation 40340812
BACKGROUND: Knee osteoarthritis (KOA) is a prevalent chronic disease worldwide, and traditional treatment methods lack personalized adjustment for individual patient differences and cannot meet the needs of personalized treatment.
Aged Algorithms Deep Learning Female Humans Male Middle Aged Osteoarthritis, Knee Outpatients Reinforcement, Psychology Surveys and Questionnaires
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Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance.

PloS one 40372990
This research explores the potential of combining Meta Reinforcement Learning (MRL) with Spike-Timing-Dependent Plasticity (STDP) to enhance the performance and adaptability of AI agents in Atari game settings. Our methodology leverages MRL to swiftl...
Artificial Intelligence Humans Learning Models, Neurological Neuronal Plasticity Reinforcement, Psychology
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