My Research
My research aims at helping to make
making machine learning robust and beneficial;
I work on safety and alignment of reinforcement learning agents.
My current research can be motivated by the following question:
How can we design competitive and scalable machine learning algorithms
that make sequential decisions in the absense of a reward function?
Publications
My publication list on Google Scholar
Deep reinforcement learning
Since joining DeepMind, I have been working on empirical research related to learning reward functions for deep reinforcement learning.
- Reward learning from human preferences and demonstrations in Atari
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei.
Neural Information Processing Systems, 2018.
- Learning to understand goal specifications by modelling reward
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette. 2018.
- AI Safety Gridworlds
Jan Leike, Miljan Martic, Victoria Krakovna, Pedro Ortega, Tom Everitt, Andrew Lefrancq, Laurent Orseau, Shane Legg. 2017.
- Deep Reinforcement Learning from Human Preferences
Paul F Christiano, Jan Leike, Tom B Brown, Miljan Martic, Shane Legg, Dario Amodei.
Neural Information Processing Systems, 2017.
Theoretical reinforcement learning
I wrote my dissertation on general reinforcement learning,
reinforcement learning in non-ergodic, partially observable environments.
My most intersting results are that
Bayesian reinforcement learning agents can misbehave drastically if given a bad prior,
that Thompson sampling learns to act optimially in any environment, and
a formal solution to an open problem in game theory.
If this interests you,
take a look at my short introduction to general reinforcement learning.
- Exploration Potential.
Jan Leike.
European Workshop on Reinforcement Learning, 2016.
- Nonparametric General Reinforcement Learning.
Jan Leike.
PhD Thesis, 2016.
- A Formal Solution to the Grain of Truth Problem.
Jan Leike, Jessica Taylor, and Benya Fallenstein.
Uncertainty in Artificial Intelligence, 2016.
- Thompson Sampling is Asymptotically Optimal in General Environments.
Jan Leike, Tor Lattimore, Laurent Orseau, and Marcus Hutter.
Uncertainty in Artificial Intelligence, 2016.
Best student paper award.
- Loss Bounds and Time Complexity for Speed Priors.
Daniel Filan, Jan Leike, and Marcus Hutter.
AI & Statistics, 2016.
- On the Computability of Solomonoff Induction and Knowledge-Seeking.
Jan Leike and Marcus Hutter.
Algorithmic Learning Theory, 2015.
- Solomonoff Induction Violates Nicod’s Criterion.
Jan Leike and Marcus Hutter.
Algorithmic Learning Theory, 2015.
- Sequential Extensions of Causal and Evidential Decision Theory.
Tom Everitt, Jan Leike, and Marcus Hutter.
Algorithmic Decision Theory, 2015.
Source code to the examples.
- On the Computability of AIXI.
Jan Leike and Marcus Hutter.
Uncertainty in Artificial Intelligence, 2015.
- Bad Universal Priors and Notions of Optimality.
Jan Leike and Marcus Hutter.
Conference on Learning Theory, 2015.
- A Definition of Happiness for Reinforcement Learning Agents.
Mayank Daswani and Jan Leike.
Artificial General Intelligence, 2015.
- Indefinitely Oscillating Martingales.
Jan Leike and Marcus Hutter.
Algorithmic Learning Theory, 2014.
Software Verification
During my Master’s degree at the University of Freiburg
I developed the termination analysis tool
Ultimate LassoRanker
together with Matthias Heizmann.
This tool can automatically prove termination and nontermination properties of C programs.
It won two second places and two first places in the termination category of the SV-COMP from 2015 to 2018.
The following papers are mostly related to that work.
- Geometric Nontermination Arguments.
Jan Leike and Matthias Heizmann.
Tools and Algorithms for the Construction and Analysis of Systems, 2018.
- Ranking Templates for Linear Loops.
Jan Leike and Matthias Heizmann.
Logical Methods in Computer Science, 2015.
- Geometric Series as Nontermination Arguments for Linear Lasso Programs.
Jan Leike and Matthias Heizmann.
International Workshop on Termination, 2014.
- Ranking Templates for Linear Loops.
Jan Leike and Matthias Heizmann.
Tools and Algorithms for the Construction and Analysis of Systems, 2014.
- Synthesis for Polynomial Lasso Programs.
Jan Leike and Ashish Tiwari.
Verification, Model Checking, and Abstract Interpretation, 2014.
Source code to the experiments.
- Linear Ranking for Linear Lasso Programs.
Matthias Heizmann,
Jochen Hoenicke,
Jan Leike, and
Andreas Podelski.
Automated Technology for Verification and Analysis, 2013.
- Ranking Function Synthesis for Linear Lasso Programs.
Jan Leike. Master’s Thesis.
University of Freiburg, 2013.
Although I take great care when polishing a paper,
sometimes technical errors remain.
Please see my list of errata.
If you find a mistake not listed there,
please let me know!