 Distributional reinforcement learning

Learning about a distribution of possible future returns rather than only its mean. Multiple prediction channels can vary in optimism and sensitivity to positive and negative errors. Such population heterogeneity can refine a reward prediction error account without implying that every dopamine signal performs the same computation.