Search Results for author: Philip Bontrager

Found 11 papers, 7 papers with code

Learning Controllable Content Generators

1 code implementation6 May 2021 Sam Earle, Maria Edwards, Ahmed Khalifa, Philip Bontrager, Julian Togelius

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.

Game Mechanic Alignment Theory and Discovery

no code implementations20 Feb 2021 Michael Cerny Green, Ahmed Khalifa, Philip Bontrager, Rodrigo Canaan, Julian Togelius

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 to Generate Levels From Nothing

1 code implementation12 Feb 2020 Philip Bontrager, Julian Togelius

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.

Image Generation

Rotation, Translation, and Cropping for Zero-Shot Generalization

1 code implementation27 Jan 2020 Chang Ye, Ahmed Khalifa, Philip Bontrager, Julian Togelius

Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games.


PCGRL: Procedural Content Generation via Reinforcement Learning

6 code implementations24 Jan 2020 Ahmed Khalifa, Philip Bontrager, Sam Earle, Julian Togelius

We investigate how reinforcement learning can be used to train level-designing agents.

Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation

1 code implementation28 Jun 2018 Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, Sebastian Risi

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.

Dimensionality Reduction

Deep Reinforcement Learning for General Video Game AI

1 code implementation6 Jun 2018 Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana

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.

Atari Games OpenAI Gym

Deep Interactive Evolution

1 code implementation24 Jan 2018 Philip Bontrager, Wending Lin, Julian Togelius, Sebastian Risi

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).

Image Generation

Deep Learning for Video Game Playing

no code implementations25 Aug 2017 Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi

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.

Real-Time Strategy Games

DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution

no code implementations21 May 2017 Philip Bontrager, Aditi Roy, Julian Togelius, Nasir Memon, Arun Ross

The proposed method, referred to as Latent Variable Evolution, is based on training a Generative Adversarial Network on a set of real fingerprint images.

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