  We focus on reinforcement learning (RL) in relational problems that are
naturally defined in terms of objects, their relations, and object-centric
actions. These problems are characterized by variable state and action spaces,
and finding a fixed-length representation, required by most existing RL
methods, is difficult, if not impossible. We present a deep RL framework based
on graph neural networks and auto-regressive policy decomposition that
naturally works with these problems and is completely domain-independent. We
demonstrate the framework's broad applicability in three distinct domains and
show impressive zero-shot generalization over different problem sizes.
