Efficient Contextual Bandits With Continuous Actions

NeurIPS 2020 |

Organized by ACM

We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure. Our reduction-style algorithm composes with most supervised learning representations. We prove that it works in a general sense and verify the new functionality with large-scale experiments.