398 citations · 1.8k across the 163 of their papers we have counts for
7 papers · 1 filter
Feature Dynamic Bayesian Networks
Marcus Hutter
Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple envir…
Feature Markov Decision Processes
Marcus Hutter
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-devel…
On the Possibility of Learning in Reactive Environments with Arbitrary Dependence
Daniil Ryabko, Marcus Hutter
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions, i.e. environments mo…
Temporal Difference Updating without a Learning Rate
Marcus Hutter, Shane Legg
We derive an equation for temporal difference learning from statistical principles. Specifically, we start with the variational principle and then bootstrap to produce an updating…
Algorithmic information theory
Peter D. Grunwald, Paul M. B. Vitanyi
We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain the main concepts of this quantitative approach to defining `information'…
Predictive Hypothesis Identification
Marcus Hutter
While statistics focusses on hypothesis testing and on estimating (properties of) the true sampling distribution, in machine learning the performance of learning algorithms on futu…