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

Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy Decomposition