mlsanityData to 14 Oct 2026

G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning

arXiv 2407.10483 ↗First posted 15 Jul 2024cs.LGcited by 4

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 33 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

14 of 33 references.

References · 14

  1. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
  2. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 200
  3. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
  4. 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
  5. 2020Deep Learning for Procedural Content Generationcited by 73
  6. 2023MarioGPT: Open-Ended Text2Level Generation through Large Language Modelscited by 64
  7. 2021Learning Controllable Content Generatorscited by 40
  8. 2021Adversarial Reinforcement Learning for Procedural Content Generationcited by 36
  9. 2020TOAD-GAN: Coherent Style Level Generation from a Single Examplecited by 22
  10. 2022Learning Controllable 3D Level Generatorscited by 19
  11. 2020Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zeldacited by 8
  12. 2022TropeTwist: Trope-based Narrative Structure Generationcited by 8
2 more
  1. 2023Balancing of competitive two-player Game Levels with Reinforcement Learningcited by 4
  2. 2024GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithmscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generationcited by 4
  2. 2025PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learningcited by 3
  3. 2025Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representationcited by 2
  4. 2025Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators
  2. 2025Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs
  3. 2023PCGPT: Procedural Content Generation via Transformers
  4. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
  5. 2026Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
  6. 2025Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
  7. 2026RLGT: A reinforcement learning framework for extremal graph theory
  8. 2025Using Reinforcement Learning to Optimize the Global and Local Crossing Number