Procedural content generation (PCG) refers to the practice of generating game content, such
as levels, quests or characters, algorithmically. Motivated by the need to make games …

The ability of deep learning to process vast data and uncover concealed malicious patterns
has spurred the adoption of deep learning methods within the cybersecurity domain …

Training RL agents to solve novel environments is a notoriously difficult task. Here we
present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural …

It has recently been shown that reinforcement learning can be used to train generators
capable of producing high-quality game levels, with quality defined in terms of some user …

In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative
Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that …

Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for
large human-authored data-sets and allows agents to train explicitly on functional …

Abstract The combination of Reinforcement Learning (RL) with deep learning has led to a
series of impressive feats, with many believing (deep) RL provides a path towards generally …

Deep learning for procedural level generation has been explored in many recent works,
however, experimental comparisons with previous works are rare and usually limited to the …

Procedural Content Generation (PCG) algorithms enable the automatic generation of
complex and diverse artifacts. However, they don't provide high-level control over the …

Procedural content generation (PCG) is a growing field, with numerous applications in the
video game industry and great potential to help create better games at a fraction of the cost …