Contextual Bandits with Continuous Actions

This paper introduces a new algorithm for contextual bandit problems where the action space is continuous. It combines ideas from bandit algorithms and supervised learning to efficiently explore the action space and learn the optimal action to take in each context. The algorithm is particularly relevant to systems that automatically optimize advertising campaigns or personalized recommendations, as it allows for fine-grained control over the actions taken.