Machine Learning for Playable Room Generation: A Modular Approach with VAE and PCG - Paes, Juan; Junio, Roberto; Rodriguez, Luis Cuevas (Anais do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames); 2025: Anais do XXIV Simpósio Brasileiro de Jogos e Entretenimento DigitalDO - 10.5753/sbgames.2025.10172, 2025)
Introduction: The growing complexity of roguelike games demands smarter content generation, but uncontrolled PCG can result in unbalanced and frustrating gameplay. Objective: This paper proposes a modular system that integrates PCG with Machine Learning (ML) to generate optimized, playable rooms for roguelike games. Methodology: The approach combines Cellular Automata for initial generation and a Variational Autoencoder (VAE) to refine layouts based on user-defined criteria. Results: The system achieved (85%) player approval for VAE-generated rooms, reduced generation time by (58%), and improved diversity, consistency, and gameplay accessibility.