An anytime algorithm for constrained stochastic shortest path problems with deterministic policies.

Sequential decision-making problems arise in every arena of daily life and pose unique challenges for research in decision-theoretic planning. Although there has been a wide variety of research in this field, most of the studies have largely focused on single objective problem without constraints. In many real-world applications, however, it is often desirable to bound certain costs or resources under some predefined level. Constrained stochastic shortest path problem (C-SSP), one of the most well-known mathematical frameworks for stochastic decision-making problems with constraints, can formally model such problems, by incorporating constraints in the model formulation. However, it remains an open challenge to produce a deterministic optimal policy with desirable computation time due to its intrinsic complexity.