Search Results for author:
Found 11 papers, 7 papers with code
DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
The proposed method, referred to as Latent Variable Evolution, is based on training a Generative Adversarial Network on a set of real fingerprint images.
Deep Learning for Video Game Playing
In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games.
Deep Interactive Evolution
The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i. e. most produced phenotypes do resemble valid domain artifacts).
Deep Reinforcement Learning for General Video Game AI
In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems.
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels.
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games
Deep reinforcement learning has learned to play many games well, but failed on others.
PCGRL: Procedural Content Generation via Reinforcement Learning
We investigate how reinforcement learning can be used to train level-designing agents.
Rotation, Translation, and Cropping for Zero-Shot Generalization
Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games.
Learning to Generate Levels From Nothing
Unlike previous approaches to procedural content generation, Generative Playing Networks are end-to-end differentiable and do not require human-designed examples or domain knowledge.
Game Mechanic Alignment Theory and Discovery
We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations.
Learning Controllable Content Generators
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-specified heuristic.
