Optimal Online Learning for Nonlinear Belief Models Using Discrete Priors

We consider an optimal learning problem where we are trying to learn a function that is nonlinear in unknown parameters in an online setting. We formulate the problem as a dynamic program, provide the optimality condition using Bellman’s equation, and propose a multiperiod lookahead policy to overcome the nonconcavity in the value of information. We adopt a sampled belief model, which we refer to as a discrete prior. For an infinite-horizon problem with discounted cumulative rewards, we prove asymptotic convergence properties under the proposed policy, a rare result for online learning. We then demonstrate the approach in three different settings: a health setting where we make medical decisions to maximize healthcare response over time, a dynamic pricing setting where we make pricing decisions to maximize the cumulative revenue, and a clinical pharmacology setting where we make dosage controls to minimize the deviation between actual and target effects.