1.2k citations · 1.3k across the 10 of their papers we have counts for
12 papers
General Uncertainty Estimation with Delta Variances
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is however challenging to estimate efficie…
Exploration via Epistemic Value Estimation
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions -- for instance…
Chaining Value Functions for Off-Policy Learning
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its e…
Learning and Planning in Complex Action Spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou +3
Many important real-world problems have action spaces that are high-dimensional, continuous or both, making full enumeration of all possible actions infeasible. Instead, only small…
Muesli: Combining Improvements in Policy Optimization
Matteo Hessel, Ivo Danihelka, Fabio Viola +6
We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-th…
AlgebraNets
Jordan Hoffmann, Simon Schmitt, Simon Osindero +2
Neural networks have historically been built layerwise from the set of functions in , i.e. with activations and weights/parameters represented…