Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
TD(λ) Converges with Probability 1
Machine Learning
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dopamine: generalization and bonuses
Neural Networks - Computational models of neuromodulation
Temporal Difference Model Reproduces Anticipatory Neural Activity
Neural Computation
Journal of Cognitive Neuroscience
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
Dopamine: generalization and bonuses
Neural Networks - Computational models of neuromodulation
Neuromodulation and Plasticity in an autonomous robot
Neural Networks - Computational models of neuromodulation
Shifting Attention Using a Temporal Difference Prediction Error and High-Dimensional Input
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Modeling dopamine activity by Reinforcement Learning methods: implications from two recent models
Artificial Intelligence Review
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
Neural affective decision theory: Choices, brains, and emotions
Cognitive Systems Research
Hi-index | 0.00 |
This article focuses on recent modeling studies of dopamine neuron activity and their influence on behavior. Activity of midbrain dopamine neurons is phasically increased by stimuli that increase the animal's reward expectation and is decreased below baseline levels when the reward fails to occur. These characteristics resemble the reward prediction error signal of the temporal difference (TD) model, which is a model of reinforcement learning. Computational modeling studies show that such a dopamine-like reward prediction error can serve as a powerful teaching signal for learning with delayed reinforcement, in particular for learning of motor sequences.Several lines of evidence suggest that dopamine is also involved in 'cognitive' processes that are not addressed by standard TD models. I propose the hypothesis that dopamine neuron activity is crucial for planning processes, also referred to as 'goal-directed behavior', which select actions by evaluating predictions about their motivational outcomes.