J
Jialin Liu
Researcher at Southern University of Science and Technology
Publications - 174
Citations - 1685
Jialin Liu is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 20, co-authored 80 publications receiving 1178 citations. Previous affiliations of Jialin Liu include French Institute for Research in Computer Science and Automation & University of Essex.
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Journal ArticleDOI
General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms
TL;DR: This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVgaI framework in research, education, and competitions five years after its birth.
Proceedings ArticleDOI
Evolving mario levels in the latent space of a deep convolutional generative adversarial network
TL;DR: In this paper, a GAN is used to generate levels for Super Mario Bros using a level from the Video Game Level Corpus, which is further improved by application of the covariance matrix adaptation evolution strategy (CMA-ES).
Posted Content
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network.
TL;DR: This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus, and uses the champion A* agent from the 2009 Mario AI competition to assess whether a level is playable, and how many jumping actions are required to beat it.
Proceedings ArticleDOI
Deep Reinforcement Learning for General Video Game AI
TL;DR: In this article, the authors describe how they interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems, and characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of games written in a domain specific description language.
Journal ArticleDOI
Deep Learning for Procedural Content Generation
Jialin Liu,Sam Snodgrass,Ahmed Khalifa,Sebastian Risi,Georgios N. Yannakakis,Julian Togelius +5 more
TL;DR: This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deeplearning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.