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We introduce variational sequential Optimal Experimental Design (vsOED), anew method for optimally designing a finite sequence of experiments under aBayesian framework and with information-gain utilities. Specifically, we adopta lower bound estimator for the expected utility through variationalapproximation to the Bayesian posteriors. The optimal design policy is solvednumerically by simultaneously maximizing the variational lower bound andperforming policy gradient updates. We demonstrate this general methodology fora range of OED problems targeting parameter inference, model discrimination,and goal-oriented prediction. These cases encompass explicit and implicitlikelihoods, nuisance parameters, and physics-based partial differentialequation models. Our vsOED results indicate substantially improved sampleefficiency and reduced number of forward model simulations compared to previoussequential design algorithms.

Variational Sequential Optimal Experimental Design using Reinforcement Learning