4 citations · 6 across the 4 of their papers we have counts for
5 papers
Improving Conditional Level Generation using Automated Validation in Match-3 Games
Monica Villanueva Aylagas, Joakim Bergdahl, Jonas Gillberg +3
Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the gener…
Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games
Carlos Garcia Ling, Konrad Tollmar, Linus Gisslen
In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB)…
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games
Jonas Gillberg, Joakim Bergdahl, Alessandro Sestini +2
Going from research to production, especially for large and complex software systems, is fundamentally a hard problem. In large-scale game production, one of the main reasons is th…
Augmenting Automated Game Testing with Deep Reinforcement Learning
Joakim Bergdahl, Camilo Gordillo, Konrad Tollmar +1
General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement…
Imitation Learning with Concurrent Actions in 3D Games
Jack Harmer, Linus Gisslén, Jorge del Val +5
In this work we describe a novel deep reinforcement learning architecture that allows multiple actions to be selected at every time-step in an efficient manner. Multi-action polici…