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Poster
Risk-Aversion in Multi-armed Bandits
Amir Sani · Alessandro Lazaric · Remi Munos

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
in Poster Session »

In stochastic multi--armed bandits the objective is to 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 intrinsically 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 introduce two new algorithms, we investigate their theoretical guarantees, and we report preliminary empirical results.

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Author Information

Amir Sani (Centre d'Economie de la Sorbonne, CNRS)
Alessandro Lazaric (Facebook Artificial Intelligence Research)
Remi Munos (Google DeepMind)

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