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Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Mohammad Gheshlaghi azar · Alessandro Lazaric · Emma Brunskill

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinforcement learning show that transfer may significantly improve the learning performance, most of the literature on transfer is focused on batch learning tasks. In this paper we study the problem of sequential transfer in online learning, notably in the multi-arm bandit framework, where the objective is to minimize the cumulative regret over a sequence of tasks by incrementally transferring knowledge from prior tasks. We introduce a novel bandit algorithm based on a method-of-moments approach for the estimation of the possible tasks and derive regret bounds for it.

Author Information

Mohammad Gheshlaghi azar (CMU)
Alessandro Lazaric (INRIA)
Emma Brunskill (CMU)

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