Bootstrapped Thompson Sampling and Deep Exploration

Ian Osband; Benjamin Van Roy

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arXiv · 1507.00300

Bootstrapped Thompson Sampling and Deep Exploration

Ian Osband·Benjamin Van Roy

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Abstract

This technical note presents a new approach to carrying out the kind of exploration achieved by Thompson sampling, but without explicitly maintaining or sampling from posterior distributions. The approach is based on a bootstrap technique that uses a combination of observed and artificially generated data. The latter serves to induce a prior distribution which, as we will demonstrate, is critical to effective exploration. We explain how the approach can be applied to multi-armed bandit and reinforcement learning problems and how it relates to Thompson sampling. The approach is particularly well-suited for contexts in which exploration is coupled with deep learning, since in these settings, maintaining or generating samples from a posterior distribution becomes computationally infeasible.

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Ian Osband, Benjamin Van Roy. 2015-07-01. Bootstrapped Thompson Sampling and Deep Exploration. https://arxiv.org/abs/1507.00300

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