Video Games and Machine Learning

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).