  In this paper, we give a new framework for the stochastic shortest path
problem in finite state and action spaces. Our framework generalizes both the
frameworks proposed by Bertsekas and Tsitsikli and by Bertsekas and Yu. We
prove that the problem is well-defined and (weakly) polynomial when (i) there
is a way to reach the target state from any initial state and (ii) there is no
transition cycle of negative costs (a generalization of negative cost cycles).
These assumptions generalize the standard assumptions for the deterministic
shortest path problem and our framework encapsulates the latter problem (in
contrast with prior works). In this new setting, we can show that (a) one can
restrict to deterministic and stationary policies, (b) the problem is still
(weakly) polynomial through linear programming, (c) Value Iteration and Policy
Iteration converge, and (d) we can extend Dijkstra's algorithm.
