The Knowledge Gradient Algorithm for a General Class of Online Learning Problems

We derive a one-period look-ahead policy for finiteand infinite-horizon online optimal learning problems with Gaussian rewards. Our approach is able to handle the case where our prior beliefs about the rewards are correlated, which is not handled by traditional multi-armed bandit methods. Experiments show that our KG policy performs competitively against the best known approximation to the optimal policy in the classic bandit problem, and outperforms many learning policies in the correlated case.