  We propose an implementable, feedforward neural network-based structure
preserving probabilistic numerical approximation for a generalized obstacle
problem describing the value of a zero-sum differential game of optimal
stopping with asymmetric information. The target solution depends on three
variables: the time, the spatial (or state) variable, and a variable from a
standard $(I-1)$-simplex which represents the probabilities with which the $I$
possible configurations of the game are played. The proposed numerical
approximation preserves the convexity of the continuous solution as well as the
lower and upper obstacle bounds. We show convergence of the fully-discrete
scheme to the unique viscosity solution of the continuous problem and present a
range of numerical studies to demonstrate its applicability.
