Instance-optimal PAC Algorithms for Contextual Bandits

Zhaoqi Li · Lillian Ratliff · houssam nassif · Kevin Jamieson · Lalit Jain

Hall J #839

Keywords: [ Active Learning ] [ contextual bandits ] [ Reinforcement Learning ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Wed 30 Nov 2 p.m. PST — 4 p.m. PST

Abstract: In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied. In this work, we focus on the stochastic bandit problem in the $(\epsilon,\delta)$-PAC setting: given a policy class $\Pi$ the goal of the learner is to return a policy $\pi\in \Pi$ whose expected reward is within $\epsilon$ of the optimal policy with probability greater than $1-\delta$. We characterize the first instance-dependent PAC sample complexity of contextual bandits through a quantity $\rho_{\Pi}$, and provide matching upper and lower bounds in terms of $\rho_{\Pi}$ for the agnostic and linear contextual best-arm identification settings. We show that no algorithm can be simultaneously minimax-optimal for regret minimization and instance-dependent PAC for best-arm identification. Our main result is a new instance-optimal and computationally efficient algorithm that relies on a polynomial number of calls to a cost-sensitive classification oracle.

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