Enhanced Exploration Least-Squares Methods for Optimal Stopping Problems

This letter presents an Approximate Dynamic Programming (ADP) least-squares based approach for solving optimal stopping problems with a large state space. By extending some previous work in the area of optimal stopping problems, it provides a framework for their formulation and resolution. The proposed method uses a combined on/off policy exploration mechanism, where states are generated by means of state transition probability distributions different from the ones dictated by the underlying Markov decision processes. The contraction mapping property of the associated projected Bellman operator is analysed as well as the convergence of the resulting algorithm.