Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Long-term reward prediction in TD models of the dopamine system
Neural Computation
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
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This review considers the theoretical problems facing agents that must learn and choose on the basis of reward or reinforcement that is uncertain or delayed, in implicit or procedural (stimulus-response) representational systems and in explicit or declarative (action-outcome-value) representational systems. Individual differences in sensitivity to delays and uncertainty may contribute to impulsivity and risk taking. Learning and choice with delayed and uncertain reinforcement are related but in some cases dissociable processes. The contributions to delay and uncertainty discounting of neuromodulators including serotonin, dopamine, and noradrenaline, and of specific neural structures including the nucleus accumbens core, nucleus accumbens shell, orbitofrontal cortex, basolateral amygdala, anterior cingulate cortex, medial prefrontal (prelimbic/infralimbic) cortex, insula, subthalamic nucleus, and hippocampus are examined.