Feb 5, 2019 · Title:Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting ... Abstract:We study contextual bandit learning with an ...
We study contextual bandit learning for any competitor policy class and continuous action space. We obtain two qualitatively different regret bounds.
We consider contextual bandits, a setting in which a learner repeatedly makes an action on the basis of contextual information and observes a loss for the ...
X-armed bandits. Journal of. Machine Learning Research, 2011. Robert Kleinberg. Nearly tight bounds for the continuum-armed bandit problem. In Advances in.
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Topics · Zooming Dimension · Contextual Bandits · Continuous Action Spaces · Contextual Bandit Learning · Regret Bounds · Continuous Actions ...
Abstract. We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: ...
How do we handle continuous action spaces in the contextual bandit protocol? •Contextual bandits with finite action sets well studied, regret scales with number ...
Sep 29, 2024 · Contextual bandits with continuous actions: Smoothing, zooming, and adapting. J. of Machine Learning Research (JMLR), 27(137):1–45, 2020 ...
In contextual bandit learning [6, 1, 39, 3], an agent repeatedly observes its environment, chooses an action, and receives a reward feedback, with the goal ...
Contextual bandits with continuous actions: smoothing, zooming, and adapting. ... • Recovers many existing results in contextual bandits with smooth loss.