The paper entitled “An Oracle-Guided Approach to Constrained Policy Synthesis Under Uncertainty” by Roman Andriushchenko, Milan Češka, Filip Macák (Brno University of Technology), Sebastian Junges (Radboud University) and Joost-Pieter Katoen has been accepted for publication in the Journal of Artificial Intelligence Research (JAIR). The paper present coloured Markov Decision Processes (MDPs) which describes a collection of possible policy configurations with their structural dependencies. The framework covers the synthesis of (a) programmatic policies from probabilistic program sketches and (b) finite-state controllers representing policies for partially observable MDPs (POMDPs), including decentralised POMDPs as well as constrained POMDPs. It is shown that all these synthesis problems can be cast as exploring memoryless policies in the corresponding coloured MDP.