Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks

We present a novel deep reinforcement learning framework for solving relational problems. The method operates with a symbolic representation of objects, their relations and multi-parameter actions, where the objects are the parameters. Our framework, based on graph neural networks, is completely domain-independent and can be applied to any relational problem with existing symbolic-relational representation. We show how to represent relational states with arbitrary goals, multi-parameter actions and concurrent actions. We evaluate the method on a set of three domains: BlockWorld, Sokoban and SysAdmin. The method displays impressive generalization over different problem sizes (e.g., in BlockWorld, the method trained exclusively with 5 blocks still solves 78% of problems with 20 blocks) and readiness for curriculum learning.