Empirical comparison of various reinforcement learning strategies for sequential targeted marketing

We empirically evaluate the performance of various reinforcement learning methods in applications to sequential targeted marketing. In particular we propose and evaluate a progression of reinforcement learning methods, ranging from the "direct" or "batch" methods to "indirect" or "simulation based" methods, and those that we call "semidirect" methods that fall between them. We conduct a number of controlled experiments to evaluate the performance of these competing methods. Our results indicate that while the indirect methods can perform better in a situation in which nearly perfect modeling is possible, under the more realistic situations in which the system's modeling parameters have restricted attention, the indirect methods' performance tend to degrade. We also show that semi-direct methods are effective in reducing the amount of computation necessary to attain a given level of performance, and often result in more profitable policies.