Contextual Bandits with Large Action Spaces: Made Practical
This addresses a significant gap between theory and practice in sequential decision making for applications requiring large action spaces, though it is incremental in extending existing methods to continuous settings.
The paper tackles the problem of developing efficient algorithms for contextual bandits with large, continuous action spaces, presenting the first general-purpose algorithm that achieves sample complexity, runtime, and memory independent of action space size, with superior performance and efficiency in empirical evaluations.
A central problem in sequential decision making is to develop algorithms that are practical and computationally efficient, yet support the use of flexible, general-purpose models. Focusing on the contextual bandit problem, recent progress provides provably efficient algorithms with strong empirical performance when the number of possible alternatives ("actions") is small, but guarantees for decision making in large, continuous action spaces have remained elusive, leading to a significant gap between theory and practice. We present the first efficient, general-purpose algorithm for contextual bandits with continuous, linearly structured action spaces. Our algorithm makes use of computational oracles for (i) supervised learning, and (ii) optimization over the action space, and achieves sample complexity, runtime, and memory independent of the size of the action space. In addition, it is simple and practical. We perform a large-scale empirical evaluation, and show that our approach typically enjoys superior performance and efficiency compared to standard baselines.