Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health. (arXiv:2209.04356v1 [cs.LG])

In this paper, we consider a risk-averse multi-armed bandit (MAB) problem
where the goal is to learn a policy that minimizes the risk of low expected
return, as opposed to maximizing the expected return itself, which is the
objective in the usual approach to risk-neutral MAB. Specifically, we formulate
this problem as a transfer learning problem between an expert and a learner
agent in the presence of contexts that are only observable by the expert but
not by the learner. …

arxiv case case study emotion health mobile multi-armed bandits regulation risk study