https://www.um.edu.mt/library/oar/handle/123456789/19740 2026-06-15T00:43:41Z https://www.um.edu.mt/library/oar/handle/123456789/147356 Title: MOPCGRL : Multi-objective procedural content generation via reinforcement learning Authors: Yuan, Yi; Zhang, Qingquan; Yuan, Bo; Barthet, Matthew; Khalifa, Ahmed; Yannakakis, Georgios N.; Chen, Huanhuan; Liu, Jialin Abstract: Online content generation enables automatic and adaptive creation of diverse and playable game content for maximizing player experience or testing Artificial Intelligence (AI) algorithms. Multiple diversity metrics have been formulated on different content facets in the literature, while some of them conflict with one another. Existing work addresses this multi-dimensional diversity nature by converting those metrics into one term that is further used to direct the training of content generators. However, each generator is trained to meet the preference specified by the weights and fails to fully interpret the relationships among these metrics or provide different trade-offs. This paper proposes a multi-objective procedural content generation via reinforcement learning to train a set of generators that create diverse game content in an online manner while balancing the trade-off between multiple diversity metrics with playability as a constraint. Our framework is compared with state-of-the-art approaches on the commonly used Mario-AI benchmark. Results show that our framework is capable of increasing the diversity of the generator distribution while accelerating the convergence during the early stages of model training. Our approach enables researchers, designers, and practitioners to gain a better understanding of the relationship among conflicting diversity metrics, allowing them to generate content more efficiently and accurately tailored to specific needs. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147355 Title: Evolutionary level repair Authors: Bhaumik, Debosmita; Togelius, Julian; Yannakakis, Georgios N.; Khalifa, Ahmed Abstract: We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the completeness of the level, reachability of objects, or other performance characteristics. The repair problem may also be constrained in that it can only make a small number of changes to the level. We investigate search-based solutions to the level repair problem, particularly using evolutionary and quality-diversity algorithms, with good results. This level repair method is applied to levels generated using a machine learning-based procedural content generation (PCGML) method that generates stylistically appropriate but frequently broken levels. This combination of PCGML for generation and search-based methods for repair shows great promise as a hybrid procedural content generation (PCG) method. 2025-07-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147351 Title: Scalable procedural content generation via transfer reinforcement learning Authors: Müller-Brockhausen, Matthias; Khalifa, Ahmed; Preuss, Mike Abstract: Procedural Content Generation algorithms that make use of Machine Learning have garnered significant attention from the general public due to their ability to generate text, images, or video content that is often indistinguishable from human-crafted artworks. These achievements typically necessitate a substantial quantity of data, which may be scarce in specialized domains such as game-level design. One machine learning technique, Reinforcement Learning (RL), can be employed to learn from trial and error to address this scarcity. However, the RL training process requires a substantial time investment and may prove unsuccessful in the case of sparse reward structures. This paper empirically demonstrates the efficacy of curriculum learning as a viable solution to address scalability issues inherent to learning more complex tasks. Instead of trying to learn the whole space from scratch, we employ transfer learning on a curriculum from small to larger levels. We empirically validated this in a 3D vector-based environment, where the objective is to generate free-form tracks that facilitate a rider to move between designated points in the same vain as the flash game hit “Linerider”. 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147344 Title: Closing the affective loop via experience-driven reinforcement learning designers Authors: Barthet, Matthew; Branco, Diogo; Gallotta, Roberto; Khalifa, Ahmed; Yannakakis, Georgios N. Abstract: Autonomously tailoring content to a set of pre-determined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experience-driven procedural content generation framework realises this vision by searching for content that elicits a certain experience pattern to a user. In this paper, we propose a novel reinforcement learning (RL) framework for generating affect-tailored content, and we test it in the domain of racing games. Specifically, the experience-driven RL (EDRL) framework is given a target arousal trace, and it then generates a racetrack that elicits the desired affective responses for a particular type of player. EDRL leverages a reward function that assesses the affective pattern of any generated racetrack from a corpus of arousal traces. Our findings suggest that EDRL can accurately generate affect-driven racing game levels according to a designer's style and outperforms search-based methods for personalised content generation. The method is not only directly applicable to game content generation tasks but also employable broadly to any domain that uses content for affective adaptation. 2024-09-01T00:00:00Z