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Conference Papers Year : 2012

Risk-Aversion in Multi-armed Bandits

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Amir Sani
Alessandro Lazaric
Rémi Munos
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Abstract

Stochastic multi--armed bandits solve the Exploration--Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expected reward is not the most desirable objective. In this paper, we introduce a novel setting based on the principle of risk--aversion where the objective is to compete against the arm with the best risk--return trade--off. This setting proves to be more difficult than the standard multi-arm bandit setting due in part to an exploration risk which introduces a regret associated to the variability of an algorithm. Using variance as a measure of risk, we define two algorithms, investigate their theoretical guarantees, and report preliminary empirical results.
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Dates and versions

hal-00772609 , version 1 (10-01-2013)

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  • HAL Id : hal-00772609 , version 1

Cite

Amir Sani, Alessandro Lazaric, Rémi Munos. Risk-Aversion in Multi-armed Bandits. NIPS - Twenty-Sixth Annual Conference on Neural Information Processing Systems, Dec 2012, Lake Tahoe, United States. ⟨hal-00772609⟩
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