Moreover, our methods aretheoretically guaranteed toconvergetothePareto front.

Decision making in these systems has a long history, yet, if the state is not fully observed acting optimally in such systems is notoriously hard.

Weformulate the decoding process asanoptimization problem which allows for multiple attributesweaimtocontrol tobeeasilyincorporated asdifferentiable constraints to the optimization. By relaxing this discrete optimization to a continuous one, we make use of Lagrangian multipliers and gradient-descent based techniques to generate the desired text.