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Reinforcement, Psychology

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Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning.

Neural computation
There is growing interest in predictive coding as a model of how the brain learns through predictions and prediction errors. Predictive coding models have traditionally focused on sensory coding and perception. Here we introduce active predictive cod...

Hierarchical Reinforcement Learning, Sequential Behavior, and the Dorsal Frontostriatal System.

Journal of cognitive neuroscience
To effectively behave within ever-changing environments, biological agents must learn and act at varying hierarchical levels such that a complex task may be broken down into more tractable subtasks. Hierarchical reinforcement learning (HRL) is a comp...

Replay in Deep Learning: Current Approaches and Missing Biological Elements.

Neural computation
Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a cr...

A reinforcement learning model to inform optimal decision paths for HIV elimination.

Mathematical biosciences and engineering : MBE
The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corre...

Computational evidence for hierarchically structured reinforcement learning in humans.

Proceedings of the National Academy of Sciences of the United States of America
Humans have the fascinating ability to achieve goals in a complex and constantly changing world, still surpassing modern machine-learning algorithms in terms of flexibility and learning speed. It is generally accepted that a crucial factor for this a...

The neurobiology of deep reinforcement learning.

Current biology : CB
In this primer, Ölveczky and Gershman review concepts and advances in deep reinforcement learning and discuss how these can inform the implementation of learning processes in biological neural networks.

Reinforcement Learning: Full Glass or Empty - Depends Who You Ask.

Current biology : CB
An extension of the prediction error theory of dopamine, imported from artificial intelligence, represents the full distribution over future rewards rather than only the average and better explains dopamine responses.

Understanding collective behaviors in reinforcement learning evolutionary games via a belief-based formalization.

Physical review. E
Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for thes...