Computing the Exact Pareto Front in Average-Cost Multi-Objective Markov Decision Processes
Many communication and control problems are cast as multi-objective Markov decision processes (MOMDPs). The complete solution to an MOMDP is the Pareto front. Much of the literature approximates this front via scalarization into single-objective MDPs. Recent work has begun to characterize the full front in discounted or simple bi-objective settings by exploiting its geometry. In this work, we characterize the exact front in average-cost MOMDPs. We show that the front is a continuous, piecewise-linear surface lying on the boundary of a convex polytope. Each vertex corresponds to a deterministic policy, and adjacent vertices differ in exactly one state. Each edge is realized as a convex com...
| Authors | Jiping Luo, Nikolaos Pappas |
|---|---|
| Primary category | eess.SY |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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