This paper examines the robustness characteristics of optimal control policies derived under the assumption of rational expectations to alternative models of expectations. We assume that agents have imperfect knowledge about the precise structure of the economy and form expectations using a forecasting model that they continuously update based on incoming data. We find that the optimal control policy derived under the assumption of rational expectations can perform poorly when expectations deviate modestly from rational expectations. We then show that the optimal control policy can be made more robust by deemphasizing the stabilization of real economic activity and interest rates relative to inflation in the central bank loss function. That is, robustness to learning provides an incentive to employ a "conservative" central banker. We then examine two types of simple monetary policy rules from the literature that have been found to be robust to model misspecification in other contexts. We find that these policies are robust to empirically plausible parameterizations of the learning models and perform about as well or better than optimal control policies.