Simple Strategies in Multi-Objective MDPs - Replication Package

We consider the verification of multiple expected reward objectives at once on Markov decision processes (MDPs).
This enables a trade-off analysis among multiple objectives by obtaining a Pareto front.
We focus on strategies that are easy to employ and implement.
That is, strategies that are pure (no randomization) and have bounded memory.
We show that checking whether a point is achievable by a pure stationary strategy is NP-complete, even for two objectives, and we provide an MILP encoding to solve the corresponding problem.
The bounded memory case is treated by a product construction.
Experimental results using Storm and Gurobi show the feasibility of our algorithms.

This artifact contains the source code of the model checker Storm (cf. stormchecker.org) as well as all required dependencies.
Moreover, we include model files and scripts for replicating the experiments as conducted for the TACAS 2020 paper.
Finally, the original logfiles which were used to produce the tables and figures in the evaluation section are included.