52 citations · 86 across the 9 of their papers we have counts for
11 papers
Limit-Computable Grains of Truth for Arbitrary Computable Extensive-Form (Un)Known Games
Cole Wyeth, Marcus Hutter, Jan Leike +1
A Bayesian player acting in an infinite multi-player game learns to predict the other players' strategies if his prior assigns positive probability to their play (or contains a gra…
Universal Reinforcement Learning Algorithms: Survey and Experiments
John Aslanides, Jan Leike, Marcus Hutter
Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal…
Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
Sean Lamont, John Aslanides, Jan Leike +1
In recent years, work has been done to develop the theory of General Reinforcement Learning (GRL). However, there are few examples demonstrating these results in a concrete way. In…
Loss Bounds and Time Complexity for Speed Priors
Daniel Filan, Marcus Hutter, Jan Leike
This paper establishes for the first time the predictive performance of speed priors and their computational complexity. A speed prior is essentially a probability distribution tha…
Thompson Sampling is Asymptotically Optimal in General Environments
Jan Leike, Tor Lattimore, Laurent Orseau +1
We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov,…
On the Computability of AIXI
Jan Leike, Marcus Hutter
How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are p…