SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning. (arXiv:2102.11075v3 [cs.LG] UPDATED)

In this paper, we consider risk-sensitive sequential decision-making in
Reinforcement Learning (RL). Our contributions are two-fold. First, we
introduce a novel and coherent quantification of risk, namely composite risk,
which quantifies the joint effect of aleatory and epistemic risk during the
learning process. Existing works considered either aleatory or epistemic risk
individually, or as an additive combination. We prove that the additive
formulation is a particular case of the composite risk when the epistemic risk
measure is replaced with expectation. …

arxiv ensemble learning lg reinforcement reinforcement learning sentinel uncertainty