A Survey of Risk-Aware Multi-Armed Bandits

Autor:	Vincent Y. F. Tan, Prashanth L.A., Krishna Jagannathan
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Statistics - Machine Learning Computer Science - Information Theory Information Theory (cs.IT) Machine Learning (stat.ML) Machine Learning (cs.LG)
DOI:	10.48550/arxiv.2205.05843
Popis:	In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This survey aims to consolidate and summarise the existing research on risk measures, specifically in the context of multi-armed bandits. We review various risk measures of interest, and comment on their properties. Next, we review existing concentration inequalities for various risk measures. Then, we proceed to defining risk-aware bandit problems, We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests, as well as the best-arm identification setting, which is a pure exploration problem -- both in the context of risk-sensitive measures. We conclude by commenting on persisting challenges and fertile areas for future research. Comment: 11 pages; Unabridged version of a a survey paper of the same title accepted to IJCAI-ECAI, 2022
Databáze:	OpenAIRE
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