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- W2085658907 abstract "Reinforcement learning algorithms have provided some of the most influential computational theories for behavioral learning that depends on reward and penalty. After briefly reviewing supporting experimental data, this paper tackles three difficult theoretical issues that remain to be explored. First, plain reinforcement learning is much too slow to be considered a plausible brain model. Second, although the temporal-difference error has an important role both in theory and in experiments, how to compute it remains an enigma. Third, function of all brain areas, including the cerebral cortex, cerebellum, brainstem and basal ganglia, seems to necessitate a new computational framework. Computational studies that emphasize meta-parameters, hierarchy, modularity and supervised learning to resolve these issues are reviewed here, together with the related experimental data." @default.
- W2085658907 created "2016-06-24" @default.
- W2085658907 creator A5028840283 @default.
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- W2085658907 date "2007-04-01" @default.
- W2085658907 modified "2023-10-16" @default.
- W2085658907 title "Efficient reinforcement learning: computational theories, neuroscience and robotics" @default.
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- W2085658907 doi "https://doi.org/10.1016/j.conb.2007.03.004" @default.
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