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Poster
Design of Experiments for Stochastic Contextual Linear Bandits
Andrea Zanette · Kefan Dong · Jonathan N Lee · Emma Brunskill

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @
in Poster Session 1 »

In the stochastic linear contextual bandit setting there exist several minimax procedures for exploration with policies that are reactive to the data being acquired. In practice, there can be a significant engineering overhead to deploy these algorithms, especially when the dataset is collected in a distributed fashion or when a human in the loop is needed to implement a different policy. Exploring with a single non-reactive policy is beneficial in such cases. Assuming some batch contexts are available, we design a single stochastic policy to collect a good dataset from which a near-optimal policy can be extracted. We present a theoretical analysis as well as numerical experiments on both synthetic and real-world datasets.

Keywords: Reinforcement Learning and Planning · Bandits

Author Information

Andrea Zanette (Stanford University)
Kefan Dong (Stanford University)
Jonathan N Lee (Stanford University)
Emma Brunskill (Stanford University)

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