no code implementations • 5 Mar 2021 • Grégoire Déletang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega
As machine learning systems become more powerful they also become increasingly unpredictable and opaque.
2 code implementations • 23 Oct 2020 • Tim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega
Probability trees are one of the simplest models of causal generative processes.
no code implementations • NeurIPS 2020 • Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro A. Ortega
Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.
no code implementations • NeurIPS 2020 • Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg
To avoid this interference incentive, we introduce a baseline policy that represents a default course of action (such as doing nothing), and use it to filter out future tasks that are not achievable by default.
no code implementations • ICLR 2019 • Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli
Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation.
3 code implementations • 19 Nov 2018 • Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg
One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions.
no code implementations • 4 Jun 2018 • Victoria Krakovna, Laurent Orseau, Ramana Kumar, Miljan Martic, Shane Legg
How can we design safe reinforcement learning agents that avoid unnecessary disruptions to their environment?
2 code implementations • 27 Nov 2017 • Jan Leike, Miljan Martic, Victoria Krakovna, Pedro A. Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg
We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
5 code implementations • NeurIPS 2017 • Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei
For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems.