The Knowledge Gradient Policy for Offline Learning with Independent Normal Rewards

We define a new type of policy, the knowledge gradient policy, in the context of an offline learning problem. We show how to compute the knowledge gradient policy efficiently and demonstrate through Monte Carlo simulations that it performs as well or better than a number of existing learning policies