Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement 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
Opponent interactions between serotonin and dopamine
Neural Networks - Computational models of neuromodulation
Long-term reward prediction in TD models of the dopamine system
Neural Computation
Reinforcement learning models of the dopamine system and their behavioral implications
Reinforcement learning models of the dopamine system and their behavioral implications
Q-learning with linear function approximation
COLT'07 Proceedings of the 20th annual conference on Learning theory
From occasional choices to inevitable musts: a computational model of nicotine addiction
Computational Intelligence and Neuroscience
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Based on the dopamine hypotheses of cocaine addiction and the assumption of decrement of brain reward system sensitivity after long-term drug exposure, we propose a computational model for cocaine addiction. Utilizing average reward temporal difference reinforcement learning, we incorporate the elevation of basal reward threshold after long-term drug exposure into the model of drug addiction proposed by Redish. Our model is consistent with the animal models of drug seeking under punishment. In the case of nondrug reward, the model explains increased impulsivity after long-term drug exposure. Furthermore, the existence of a blocking effect for cocaine is predicted by our model.