Simple Strategies in Multi-Objective MDPs - Replication Package

We consider the verification of multiple expected reward objectives at once on Markov decision processes (MDPs). <br>This enables a trade-off analysis among multiple objectives by obtaining a Pareto front. <br>We focus on strategies that are easy to employ and implement. <br>That is, strategies that are pure (no randomization) and have bounded memory. <br>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. <br>The bounded memory case is treated by a product construction. <br>Experimental results using Storm and Gurobi show the feasibility of our algorithms. <br> <br>This artifact contains the source code of the model checker Storm (cf. stormchecker.org) as well as all required dependencies. <br>Moreover, we include model files and scripts for replicating the experiments as conducted for the TACAS 2020 paper. <br>Finally, the original logfiles which were used to produce the tables and figures in the evaluation section are included.