Attribution-based Explanations for Markov Decision Processes

ArXi:2605.09780v1 Announce Type: new Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This paper fills this gap by