Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit |
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Authors: | Ito Makoto Doya Kenji |
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Institution: | 1 Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa 904-0412, Japan 2 Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto 619-0288, Japan |
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Abstract: | Accumulating evidence shows that the neural network of the cerebral cortex and the basal ganglia is critically involved in reinforcement learning. Recent studies found functional heterogeneity within the cortico-basal ganglia circuit, especially in its ventromedial to dorsolateral axis. Here we review computational issues in reinforcement learning and propose a working hypothesis on how multiple reinforcement learning algorithms are implemented in the cortico-basal ganglia circuit using different representations of states, values, and actions. |
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