A distributional code for value in dopamine-based reinforcement learning.

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 brain^1-3. 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 learning^4-6. 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 para

SUBMITTER: Dabney W 

PROVIDER: S-EPMC7476215 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature