398 citations · 1.8k across the 163 of their papers we have counts for
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Exact Reduction of Huge Action Spaces in General Reinforcement Learning
Sultan Javed Majeed, Marcus Hutter
The reinforcement learning (RL) framework formalizes the notion of learning with interactions. Many real-world problems have large state-spaces and/or action-spaces such as in Go,…
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Thomas Mesnard, Théophane Weber, Fabio Viola +11
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards. In particular, this requires separating skill from luck, i.e. disen…
A Combinatorial Perspective on Transfer Learning
Jianan Wang, Eren Sezener, David Budden +2
Human intelligence is characterized not only by the capacity to learn complex skills, but the ability to rapidly adapt and acquire new skills within an ever-changing environment. I…
Logarithmic Pruning is All You Need
Laurent Orseau, Marcus Hutter, Omar Rivasplata
The Lottery Ticket Hypothesis is a conjecture that every large neural network contains a subnetwork that, when trained in isolation, achieves comparable performance to the large ne…
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent
Michael K. Cohen, Elliot Catt, Marcus Hutter
Reinforcement learners are agents that learn to pick actions that lead to high reward. Ideally, the value of a reinforcement learner's policy approaches optimality--where the optim…
Online Learning in Contextual Bandits using Gated Linear Networks
Eren Sezener, Marcus Hutter, David Budden +2
We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recen…