6 papers
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
Vanessa Volz, Niels Justesen, Sam Snodgrass +5
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are…
Bootstrapping Conditional GANs for Video Game Level Generation
Ruben Rodriguez Torrado, Ahmed Khalifa, Michael Cerny Green +3
Generative Adversarial Networks (GANs) have shown im-pressive results for image generation. However, GANs facechallenges in generating contents with certain types of con-straints,…
Learning a Behavioral Repertoire from Demonstrations
Niels Justesen, Miguel Gonzalez Duque, Daniel Cabarcas Jaramillo +2
Imitation Learning (IL) is a machine learning approach to learn a policy from a dataset of demonstrations. IL can be useful to kick-start learning before applying reinforcement lea…
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager +3
Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are tr…
Automated Curriculum Learning by Rewarding Temporally Rare Events
Niels Justesen, Sebastian Risi
Reward shaping allows reinforcement learning (RL) agents to accelerate learning by receiving additional reward signals. However, these signals can be difficult to design manually,…
Learning Macromanagement in StarCraft from Replays using Deep Learning
Niels Justesen, Sebastian Risi
The real-time strategy game StarCraft has proven to be a challenging environment for artificial intelligence techniques, and as a result, current state-of-the-art solutions consist…