Optimal Exploration is no harder than Thompson Sampling

• URL: http://arxiv.org/abs/2310.06069v2
• Date: Tue, 24 Oct 2023 20:13:58 GMT
• Title: Optimal Exploration is no harder than Thompson Sampling
• Authors: Zhaoqi Li, Kevin Jamieson, Lalit Jain
• Abstract summary: A pure exploration linear bandit problem aims to return $argmax_zin mathcalZ ztoptheta_ast with high probability through noisy measurements of $xtoptheta_ast with $xin mathcalXsubset mathbbRd$. This complexity is at odds with the popular and simple Thompson Sampling for regret minimization, which just requires access to a posterior sampling and argmax oracle, and does not need to enumerate $mathcalZ$
• Score: 14.726673043806391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a set of arms $\mathcal{Z}\subset \mathbb{R}^d$ and an unknown parameter vector $\theta_\ast\in\mathbb{R}^d$, the pure exploration linear bandit problem aims to return $\arg\max_{z\in \mathcal{Z}} z^{\top}\theta_{\ast}$, with high probability through noisy measurements of $x^{\top}\theta_{\ast}$ with $x\in \mathcal{X}\subset \mathbb{R}^d$. Existing (asymptotically) optimal methods require either a) potentially costly projections for each arm $z\in \mathcal{Z}$ or b) explicitly maintaining a subset of $\mathcal{Z}$ under consideration at each time. This complexity is at odds with the popular and simple Thompson Sampling algorithm for regret minimization, which just requires access to a posterior sampling and argmax oracle, and does not need to enumerate $\mathcal{Z}$ at any point. Unfortunately, Thompson sampling is known to be sub-optimal for pure exploration. In this work, we pose a natural question: is there an algorithm that can explore optimally and only needs the same computational primitives as Thompson Sampling? We answer the question in the affirmative. We provide an algorithm that leverages only sampling and argmax oracles and achieves an exponential convergence rate, with the exponent being the optimal among all possible allocations asymptotically. In addition, we show that our algorithm can be easily implemented and performs as well empirically as existing asymptotically optimal methods.

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