Linearly parameterized bandits
We consider bandit problems involving a large (possibly infinite) collection of arms, in which the expected reward of each arm is a linear function of an r-dimensional random vector Z ∈ ℝ(superscript r), where r ≥ 2. The objective is to minimize the cumulative regret and Bayes risk. When the set of...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
INFORMS
2012
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| Online Access: | http://hdl.handle.net/1721.1/71016 https://orcid.org/0000-0003-2658-8239 |