  Graph data structures offer a versatile and powerful means to model
relationships and interconnections in various domains, promising substantial
advantages in data representation, analysis, and visualization. In games,
graph-based data structures are omnipresent and represent, for example, game
economies, skill trees or complex, branching quest lines. With this paper, we
propose G-PCGRL, a novel and controllable method for the procedural generation
of graph data using reinforcement learning. Therefore, we frame this problem as
manipulating a graph's adjacency matrix to fulfill a given set of constraints.
Our method adapts and extends the Procedural Content Generation via
Reinforcement Learning (PCGRL) framework and introduces new representations to
frame the problem of graph data generation as a Markov decision process. We
compare the performance of our method with the original PCGRL, the run time
with a random search and evolutionary algorithm, and evaluate G-PCGRL on two
graph data domains in games: game economies and skill trees. The results show
that our method is capable of generating graph-based content quickly and
reliably to support and inspire designers in the game creation process. In
addition, trained models are controllable in terms of the type and number of
nodes to be generated.
