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Reward

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Reward-predictive representations generalize across tasks in reinforcement learning.

PLoS computational biology
In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural netwo...

Recovery of reward function in problematic substance users using a combination of robotics, electrophysiology, and TMS.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
BACKGROUND: Theoretical and empirical work suggest that addictive drugs potentiate dopaminergic reinforcement learning signals and disrupt the reward function of its neural targets, including the anterior midcingulate cortex (aMCC) and the basal gang...

PORF-DDPG: Learning Personalized Autonomous Driving Behavior with Progressively Optimized Reward Function.

Sensors (Basel, Switzerland)
Autonomous driving with artificial intelligence technology has been viewed as promising for autonomous vehicles hitting the road in the near future. In recent years, considerable progress has been made with Deep Reinforcement Learnings (DRLs) for rea...

Preschoolers' Motivation to Over-Imitate Humans and Robots.

Child development
From preschool age, humans tend to imitate causally irrelevant actions-they over-imitate. This study investigated whether children over-imitate even when they know a more efficient task solution and whether they imitate irrelevant actions equally fro...

Adaptive balancing of exploration and exploitation around the edge of chaos in internal-chaos-based learning.

Neural networks : the official journal of the International Neural Network Society
This paper addresses learning with exploration driven by chaotic internal dynamics of a neural network. Hoerzer et al. showed that a chaotic reservoir network (RN) can learn with exploration driven by external random noise and a sequential reward. In...

Modeling motivation for alcohol in humans using traditional and machine learning approaches.

Addiction biology
Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely bee...

Experimentally revealed stochastic preferences for multicomponent choice options.

Journal of experimental psychology. Animal learning and cognition
Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent c...

Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Psychopharmacology
RATIONALE: Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior th...

Distinct signals in medial and lateral VTA dopamine neurons modulate fear extinction at different times.

eLife
Dopamine (DA) neurons are to encode reward prediction error (RPE), in addition to other signals, such as salience. While RPE is known to support learning, the role of salience in learning remains less clear. To address this, we recorded and manipulat...

Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

Neural networks : the official journal of the International Neural Network Society
Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. How...