Solving Constrained Stochastic Shortest Path Problems with Scalarisation
This addresses constrained stochastic shortest path problems for applications like planning under probabilistic effects and resource constraints, representing a strong specific gain.
The paper tackles constrained stochastic shortest path problems by introducing the CARL algorithm, which solves a series of unconstrained subproblems using scalarisation, resulting in solving 50% more problems than the state-of-the-art on existing benchmarks.
Constrained Stochastic Shortest Path Problems (CSSPs) model problems with probabilistic effects, where a primary cost is minimised subject to constraints over secondary costs, e.g., minimise time subject to monetary budget. Current heuristic search algorithms for CSSPs solve a sequence of increasingly larger CSSPs as linear programs until an optimal solution for the original CSSP is found. In this paper, we introduce a novel algorithm CARL, which solves a series of unconstrained Stochastic Shortest Path Problems (SSPs) with efficient heuristic search algorithms. These SSP subproblems are constructed with scalarisations that project the CSSP's vector of primary and secondary costs onto a scalar cost. CARL finds a maximising scalarisation using an optimisation algorithm similar to the subgradient method which, together with the solution to its associated SSP, yields a set of policies that are combined into an optimal policy for the CSSP. Our experiments show that CARL solves 50% more problems than the state-of-the-art on existing benchmarks.