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Workshop
Video Games and Machine Learning
Gabriel Synnaeve · Julian Togelius · Tom Schaul · Oriol Vinyals · Nicolas Usunier

Wed Aug 09 03:30 PM -- 12:30 AM (PDT) @ C4.6
Event URL: https://syhw.github.io/vgml_workshop_icml2017/ »

Good benchmarks are necessary for developing artificial intelligence. Recently, there has been a growing movement for the use of video games as machine learning benchmarks [1,2,3], and also an interest in the applications of machine learning from the video games community. While games have been used for AI research for a long time, only recently have we seen modern machine learning methods applied to video games.

This workshop focuses on complex games which provide interesting and hard challenges for machine learning. Going beyond simple toy problems of the past, and games which can easily be solved with search, we focus on games where learning is likely to be necessary to play well. This includes strategy games such as StarCraft [4,5], open-world games such as MineCraft [6,7,8], first-person shooters such as Doom [9,10], as well as hard and unsolved 2D games such as Ms. Pac-Man and Montezuma's Revenge [11,12,13]. While we see most of the challenges in game-playing, there are also interesting machine learning challenges in modeling and content generation [14]. This workshop aims at bringing together all researchers from ICML who want to use video games as a benchmark. We will have talks by invited speakers from machine learning, from the game AI community, and from the video games industry.

[1] Greg Brockman, Catherine Olsson, Alex Ray, et al. "OpenAI Universe", https://openai.com/blog/universe/ (2016).
[2] Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, Stig Petersen, "DeepMind Lab", arXiv:1612.03801 (2016).
[3] Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier, "TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games", arXiv:1611.00625 (2016).
[4] Santiago Ontanon, Gabriel Synnaeve, Alberto Uriarte, Florian Richoux, David Churchill, Mike Preuss, "A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft", IEEE Transactions on Computational Intelligence and AI in games 5.4 (2013): 293-311.
[5] StarCraft AI Competition @ AIIDE 2016
[6] Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, and Honglak Lee, "Control of Memory, Active Perception, and Action in Minecraft", ICML (2016).
[7] Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor, "A Deep Hierarchical Approach to Lifelong Learning in Minecraft", arXiv preprint arXiv:1604.07255 (2016).
[8] Matthew Johnson, Katja Hofmann, Tim Hutton, David Bignell, "The Malmo Platform for Artificial Intelligence Experimentation", IJCAI (2016).
[9] Visual Doom AI Competition @ CIG 2016
[10] Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy P. Lillicrap, David Silver, Koray Kavukcuoglu, "Asynchronous Methods for Deep Reinforcement Learning", arXiv preprint arXiv:1602.01783 (2016).
[11] Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum, "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation", arXiv prepint arXiv:1604.06057 (2016).
[12] Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos, "Unifying Count-Based Exploration and Intrinsic Motivation", arXiv preprint arXiv:1606.01868 (2016).
[13] Diego Perez-Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon Lucas, "General Video Game AI: Competition, Challenges and Opportunities", AAAI (2016).
[14] Julian Togelius, Georgios N. Yannakakis, Kenneth O. Stanley and Cameron Browne, "Search-based Procedural Content Generation: a Taxonomy and Survey". IEEE TCIAIG (2011).

Author Information

Gabriel Synnaeve (Facebook AI Research)
Julian Togelius (New York University)

Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation in games, general video game playing, player modeling, and fair and relevant benchmarking of AI through game-based competitions. He is a past chair of the IEEE CIS Technical Committee on Games, and an associate editor of IEEE Transactions on Computational Intelligence and Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex. He has previously worked at IDSIA in Lugano and at the IT University of Copenhagen.

Tom Schaul (Google DeepMind)
Oriol Vinyals (Google DeepMind)

Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.

Nicolas Usunier (Facebook AI Research)

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