AIMC Topic: Reinforcement, Psychology

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Incremental model-based reinforcement learning with model constraint.

Neural networks : the official journal of the International Neural Network Society Feb 8, 2025
In model-based reinforcement learning (RL) approaches, the estimated model of a real environment is learned with limited data and then utilized for policy optimization. As a result, the policy optimization process in model-based RL is influenced by b...
Algorithms Learning Reinforcement, Psychology Humans Neural Networks, Computer
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Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports.

European journal of radiology Feb 6, 2025
BACKGROUND: Large language models (LLMs) often struggle to fully capture the nuanced preferences and clinical judgement of radiologists in medical report summarization even when fine-tuned on massive medical reports. This could lead to the generated ...
Radiography, Thoracic Large Language Models Reinforcement, Psychology Middle Aged Tomography, X-Ray Computed Radiologists Machine Learning Female Humans Retrospective Studies Aged Artificial Intelligence Feedback Male
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Sample-efficient and occlusion-robust reinforcement learning for robotic manipulation via multimodal fusion dualization and representation normalization.

Neural networks : the official journal of the International Neural Network Society Jan 30, 2025
Recent advances in visual reinforcement learning (visual RL), which learns from high-dimensional image observations, have narrowed the gap between state-based and image-based training. However, visual RL continues to face significant challenges in ro...
Robotics Reinforcement, Psychology Neural Networks, Computer Algorithms Humans
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A multi-agent reinforcement learning framework for cross-domain sequential recommendation.

Neural networks : the official journal of the International Neural Network Society Jan 22, 2025
Sequential recommendation models aim to predict the next item based on the sequence of items users interact with, ordered chronologically. However, these models face the challenge of data sparsity. Recent studies have explored cross-domain sequential...
Humans Neural Networks, Computer Reinforcement, Psychology
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A rule- and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs.

Neural networks : the official journal of the International Neural Network Society Jan 21, 2025
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios. Existing TKG reasoning methods primarily focus on capturing the fac...
Machine Learning Knowledge Humans Semantics Neural Networks, Computer Reinforcement, Psychology
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Simulating fish autonomous swimming behaviours using deep reinforcement learning based on Kolmogorov-Arnold Networks.

Bioinspiration & biomimetics Jan 16, 2025
The study of fish swimming behaviours and locomotion mechanisms holds significant scientific and engineering value. With the rapid advancements in artificial intelligence, a new method combining deep reinforcement learning (DRL) with computational fl...
Animals Deep Learning Artificial Intelligence Computer Simulation Hydrodynamics Reinforcement, Psychology Reinforcement Machine Learning Fishes Swimming Neural Networks, Computer
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Novel deep reinforcement learning based collision avoidance approach for path planning of robots in unknown environment.

PloS one Jan 16, 2025
Reinforcement learning is a remarkable aspect of the artificial intelligence field with many applications. Reinforcement learning facilitates learning new tasks based on action and reward principles. Motion planning addresses the navigation problem f...
Reinforcement Machine Learning Robotics Reinforcement, Psychology Artificial Intelligence Humans Algorithms Deep Learning
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Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent.

PloS one Jan 16, 2025
Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological...
Algorithms Deep Learning Models, Economic Investments Financial Management Reinforcement, Psychology Reinforcement Machine Learning Humans
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Dynamic planning in hierarchical active inference.

Neural networks : the official journal of the International Neural Network Society Jan 8, 2025
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, const...
Robotics Reinforcement, Psychology Artificial Intelligence Brain Animals Decision Making Learning Humans Neural Networks, Computer
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VCSAP: Online reinforcement learning exploration method based on visitation count of state-action pairs.

Neural networks : the official journal of the International Neural Network Society Jan 1, 2025
In the domain of online reinforcement learning, strategies that leverage inherent rewards for exploration tend to achieve commendable outcomes within contexts characterized by deceptive or sparse rewards. Counting through the visitation of states is ...
Algorithms Computer Simulation Reinforcement, Psychology Reward Humans Neural Networks, Computer
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