Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.

The goal is to efficiently compute a near-optimal policy for a given user.

However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.