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Homepage of Joel Veness

Homepage of Joel Veness

http://www.jveness.info/

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Homepage of Joel Veness | jveness.info Reviews
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Homepage of Joel Veness
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1 joel veness
2 research
3 software
4 publications
5 links
6 artificial intelligence
7 generally intelligent
8 areas
9 universal artificial intelligence
10 reinforcement learning
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Homepage of Joel Veness | jveness.info Reviews

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Homepage of Joel Veness

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1

Software

http://www.jveness.info/software/default.html

Research Scientist, Google DeepMind. An implementation of the reinforcement learning agent used to generate the results in. A Monte-Carlo AIXI Approximation. Section for more information. Context Tree Switching (2011). V10 CTS source and Windows binaries. An implementation of Context Tree Switching (CTS), a new general purpose data compressor. See here. For a technical description. Under the handle agog, with a best place finish of 2nd. Rated at 2319 18. Elo on the CGOS.

2

Publications

http://www.jveness.info/publications/default.html

Research Scientist, Google DeepMind. Human-level control through deep reinforcement learning. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness. Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis. The Arcade Learning Environment:. An Evaluation Platform for General Agents. A Monte-Carlo AIXI Approximation.

3

Homepage of Joel Veness

http://www.jveness.info/index.html

Research Scientist, Google DeepMind. Broadly speaking, I am interested in the algorithmic and computational aspects of. With a particular emphasis on scalable and efficient approaches for online Reinforcement Learning agents. Reinforcement Learning / Universal AI. Consider an agent let loose in an unknown world with only a crude reward signal and limited sensor information as feedback. An agent that can be expected to accumulate a lot of reward in many different worlds can be viewed as being.

4

About

http://www.jveness.info/about_me/default.html

Research Scientist, Google DeepMind. I am a 30-something Computer Scientist. At the moment I am working on various aspects of Monte-Carlo Tree Search, General Atari 2600 Game Playing. Universal source coding and reinforcement learning. I investigated some practical aspects of Marcus Hutter. S AIXI agent, a Bayesian optimality notion for reinforcement learning. My thesis is now available. My CV is available on request. I grew up in Penrith, later moving to Sydney to study at UNSW.

5

Links

http://www.jveness.info/links/default.html

Research Scientist, Google DeepMind. Strong AI: Selected Readings. The Collected Works of Ray Solomonoff (1926-2009). Marcus Hutters Universal Artificial Intelligence theory (AIXI). Vernor Vinges Essay on the Singularity. Achine Super Intelligence, Shane Legg. The Social Impact of Artificial Intelligence, Matt Mahoney. Hugo award winning novels. Nebula award winning novels. Forum: http:/ talkchess.com. Interesting Research Groups / Reading Lists. General Reinforcement Learning Agents.

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vveitas.wordpress.com vveitas.wordpress.com

January 2013 – Cogmach

https://vveitas.wordpress.com/2013/01

January 2, 2013. January 2, 2013. This is an old magnetic drum used as a lawn roller by my dad in his garden. The drum is a remnant from BESM-6 (or BESM-4? Mainframe computer from Soviet times, somewhere around 1970s. The capacity of the drum was 192 Kb. The jeep in the background should serve for getting the feel about the size of the thing :). Hail Moore’s law! Some links about BESM-6: on Wikipedia. January 2, 2013. The Fifth Conference on Artificial General Intelligence. Oxford, UK, December 8-11, 2012.

grla.wikidot.com grla.wikidot.com

Australian Reinforcement Learning Group - Reinforcement Learning

http://grla.wikidot.com/rlgroup

PORL NIPS 2009 Symposium. What is a Wiki Site? How to edit pages? How to join this site? Australian Reinforcement Learning Group. Welcome to the Australian Reinforcement Learning Group. The Reinforcement Learning Group is interested in developing and studying the foundations and pushing the frontier of. Intelligent agents endowed with minimal a-priori knowledge about the world, and that learn from experience, in particular for partial observable domains (PORL). See, for instance:. ARC PostDoc, Agents, RS...

grla.wikidot.com grla.wikidot.com

Universal Artificial Intelligence Reading Group - Reinforcement Learning

http://grla.wikidot.com/aixi

PORL NIPS 2009 Symposium. What is a Wiki Site? How to edit pages? How to join this site? Universal Artificial Intelligence Reading Group. Relevant information, changes, and updates will be placed on this site. Please watch. Welcome to the Universal AI Reading Group at RSISE@ANU. Actual and Potential Participants. What is Universal AI? Welcome to the Universal AI Reading Group at RSISE@ANU. The group is currently merged with the 11:30 (Wednesdays) RL reading group. Every Wednesday, 10:00-11:00. I hope you...

arcadelearningenvironment.org arcadelearningenvironment.org

» AAAI-15 Workshop The Arcade Learning Environment

http://www.arcadelearningenvironment.org/aaai15-workshop

The Arcade Learning Environment. Arcade Platform for General Artificial Intelligence development. Learning for General Competency in Video Games. AAAI 2015 Workshop, January 26, 2015, Austin, Texas, USA. 8212; Important Dates. November, 1st, 2014. November, 14, 2014. January, 26, 2015. Full submissions (4-8 pages):. Published or unpublished work applied to the Atari 2600 or other video game domains requiring general competency. Accepted work will be allotted 20 minutes for presentation, including que...

vetta.org vetta.org

AIQ | vetta project

http://www.vetta.org/2011/11/aiq

Sutton on human level AI. Goodbye 2011, hello 2012 →. 16 November, 2011. Some of you might remember the talk I gave. At the 2010 Singularity Summit about Algorithmic IQ, or AIQ for short. It was an attempt to convert the theoretical Universal Intelligence Measure. Into a working practical test of machine intelligence. The results were preliminary, but it seemed to work…. It’s now over a year later so I guess some of you are wondering what happened to AIQ! The talk slides here. Sutton on human level AI.

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Homepage of Joel Veness

Research Scientist, Google DeepMind. Broadly speaking, I am interested in the algorithmic and computational aspects of. With a particular emphasis on scalable and efficient approaches for online Reinforcement Learning agents. Reinforcement Learning / Universal AI. Consider an agent let loose in an unknown world with only a crude reward signal and limited sensor information as feedback. An agent that can be expected to accumulate a lot of reward in many different worlds can be viewed as being.

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