Reward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games.

arXiv:2610.26568v1 Announce Type: new Abstract: Robust partially observable Markov decision processes model agents that act under partial observability while adversarial environment resolves uncertainty in the transition probabilities. Reward objectives such a