How to select a sequence of experiments that maximizes value of experimental data? We formulate this optimal sequential experimental design problem by maximizing expected information gain under continuous parameter,design,and observation spaces using dynamic programming.We solve the problem numerically by using transport maps to represent posteriors and enable fast approximate Bayesian inference,and adaptive one-step look-ahead method to find the optimal policy.Results are demonstrated on sequential sensing problem for source inversion.