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A Local Temporal Difference Code for Distributional Reinforcement Learning
Recent theoretical and experimental results suggest that the dopamine system implements distributional temporal difference backups, allowing learning of the entire distributions of the long-run values of states rather than just their expected values.
Towards a more flexible Language of Thought: Bayesian grammar updates after each concept exposure
Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive rule systems and statistical learning to explain our ability to learn new concepts from just a few examples.
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