A distributional code for value in dopamine-based reinforcement learning

Will Dabney (), Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos and Matthew Botvinick
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Will Dabney: DeepMind
Zeb Kurth-Nelson: DeepMind
Naoshige Uchida: Harvard University
Clara Kwon Starkweather: Harvard University
Demis Hassabis: DeepMind
Rémi Munos: DeepMind
Matthew Botvinick: DeepMind

Nature, 2020, vol. 577, issue 7792, 671-675

Abstract: Abstract Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain1–3. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning4–6. We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

Date: 2020
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DOI: 10.1038/s41586-019-1924-6

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