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Reinforcement, Psychology

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Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning.

International journal of computer assisted radiology and surgery
PURPOSE: Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be re...

Salience Interest Option: Temporal abstraction with salience interest functions.

Neural networks : the official journal of the International Neural Network Society
Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions,...

Multimodal information bottleneck for deep reinforcement learning with multiple sensors.

Neural networks : the official journal of the International Neural Network Society
Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively from multiple sensory modalities that differ in many characteristics. Recent works construct auxiliary losses based on re...

Recurrent neural networks that learn multi-step visual routines with reinforcement learning.

PLoS computational biology
Many cognitive problems can be decomposed into series of subproblems that are solved sequentially by the brain. When subproblems are solved, relevant intermediate results need to be stored by neurons and propagated to the next subproblem, until the o...

Autoshaped impulsivity: Some explorations with a neural network model.

Behavioural processes
This study evaluated the effect of delay and magnitude of reinforcement in Pavlovian contingencies, extending the understanding of the phenomenon of autoshaped impulsivity as described in Alcalá's thesis (2017) and Burgos and García-Leal (2015). The ...

Real-world humanoid locomotion with reinforcement learning.

Science robotics
Humanoid robots that can autonomously operate in diverse environments have the potential to help address labor shortages in factories, assist elderly at home, and colonize new planets. Although classical controllers for humanoid robots have shown imp...

Emergence of integrated behaviors through direct optimization for homeostasis.

Neural networks : the official journal of the International Neural Network Society
Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeos...

A deep reinforcement learning algorithm framework for solving multi-objective traveling salesman problem based on feature transformation.

Neural networks : the official journal of the International Neural Network Society
As a special type of multi-objective combinatorial optimization problems (MOCOPs), the multi-objective traveling salesman problem (MOTSP) plays an important role in practical fields such as transportation and robot control. However, due to the comple...

Collaborative hunting in artificial agents with deep reinforcement learning.

eLife
Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings th...

Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook.

IEEE transactions on neural networks and learning systems
Human activity recognition (HAR) is a popular research field in computer vision that has already been widely studied. However, it is still an active research field since it plays an important role in many current and emerging real-world intelligent s...