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Quantifying Differences in Reward Functions

Jun 24, 2020
Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike

* 8 pages main paper, 29 pages total 

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Pitfalls of learning a reward function online

Apr 28, 2020
Stuart Armstrong, Jan Leike, Laurent Orseau, Shane Legg


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The Incentives that Shape Behaviour

Jan 20, 2020
Ryan Carey, Eric Langlois, Tom Everitt, Shane Legg

* 12 pages, 7 figures, accepted to SafeAI workshop at AAAI 

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Learning Human Objectives by Evaluating Hypothetical Behavior

Dec 05, 2019
Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike


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Modeling AGI Safety Frameworks with Causal Influence Diagrams

Jun 20, 2019
Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg

* IJCAI 2019 AI Safety Workshop 

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Meta-learning of Sequential Strategies

May 08, 2019
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg

* DeepMind Technical Report (15 pages, 6 figures) 

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Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings

Mar 12, 2019
Tom Everitt, Pedro A. Ortega, Elizabeth Barnes, Shane Legg


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Soft-Bayes: Prod for Mixtures of Experts with Log-Loss

Jan 08, 2019
Laurent Orseau, Tor Lattimore, Shane Legg

* Algorithmic Learning Theory 2017 

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Scaling shared model governance via model splitting

Dec 14, 2018
Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli

* 9 pages 

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Scalable agent alignment via reward modeling: a research direction

Nov 19, 2018
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg


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Reward learning from human preferences and demonstrations in Atari

Nov 15, 2018
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei

* NIPS 2018 

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Modeling Friends and Foes

Jun 30, 2018
Pedro A. Ortega, Shane Legg

* 13 pages, 9 figures 

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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Jun 28, 2018
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu


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Measuring and avoiding side effects using relative reachability

Jun 04, 2018
Victoria Krakovna, Laurent Orseau, Miljan Martic, Shane Legg


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Agents and Devices: A Relative Definition of Agency

May 31, 2018
Laurent Orseau, Simon McGregor McGill, Shane Legg


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Noisy Networks for Exploration

Feb 15, 2018
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

* ICLR 2018 

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Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

Feb 04, 2018
Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson, Antonio Garc√≠a Casta√Īeda, Manuel Sanchez, Simon Green, Audrunas Gruslys, Shane Legg, Demis Hassabis, Matthew M. Botvinick

* 28 pages, 11 figures 

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AI Safety Gridworlds

Nov 28, 2017
Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg


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Reinforcement Learning with a Corrupted Reward Channel

Aug 19, 2017
Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg

* A shorter version of this report was accepted to IJCAI 2017 AI and Autonomy track 

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Deep reinforcement learning from human preferences

Jul 13, 2017
Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei


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DeepMind Lab

Dec 13, 2016
Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich K√ľttler, Andrew Lefrancq, Simon Green, V√≠ctor Vald√©s, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, Stig Petersen

* 11 pages, 8 figures 

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Massively Parallel Methods for Deep Reinforcement Learning

Jul 16, 2015
Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver

* Presented at the Deep Learning Workshop, International Conference on Machine Learning, Lille, France, 2015 

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An Approximation of the Universal Intelligence Measure

Sep 29, 2011
Shane Legg, Joel Veness

* 14 pages 

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Temporal Difference Updating without a Learning Rate

Oct 31, 2008
Marcus Hutter, Shane Legg

* Advances in Neural Information Processing Systems 20 (NIPS 2008) pages 705-712 
* 12 pages, 6 figures 

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Tests of Machine Intelligence

Dec 22, 2007
Shane Legg, Marcus Hutter

* 50 Years of Artificial Intelligence (2007) pages 232-242 
* 12 pages; 1 table. Turing test and derivatives; Compression tests; Linguistic complexity; Multiple cognitive abilities; Competitive games; Psychometric tests; Smith's test; C-test; Universal intelligence 

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Universal Intelligence: A Definition of Machine Intelligence

Dec 20, 2007
Shane Legg, Marcus Hutter

* Minds & Machines, 17:4 (2007) pages 391-444 
* 50 gentle pages 

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A Collection of Definitions of Intelligence

Jun 25, 2007
Shane Legg, Marcus Hutter

* Frontiers in Artificial Intelligence and Applications, Vol.157 (2007) 17-24 
* 12 LaTeX pages 

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Fitness Uniform Optimization

Oct 20, 2006
Marcus Hutter, Shane Legg

* IEEE Transactions on Evolutionary Computation, 10:5 (2006) 568-589 
* 25 double-column pages, 12 figures 

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Is there an Elegant Universal Theory of Prediction?

Jun 14, 2006
Shane Legg


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A Formal Measure of Machine Intelligence

May 06, 2006
Shane Legg, Marcus Hutter

* Proc. 15th Annual Machine Learning Conference of {B}elgium and The Netherlands (Benelearn 2006) pages 73-80 
* 8 two-column pages 

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