Sequential Batch Learning in Finite-Action Linear Contextual Bandits
This episode discusses a paper on Sequential Batch Learning in Contextual Bandits. The research addresses how to learn optimal decisions when feedback is delayed, arriving in batches rather than instantly. The authors prove that the required number of batches depends heavily on the environment: either needing roughly the square root of total people or requiring only a tiny fraction if the data is randomly distributed.