70 citations · 101 across the 4 of their papers we have counts for
8 papers
The Impact of Negative Sampling on Contrastive Structured World Models
Ondrej Biza, Elise van der Pol, Thomas Kipf
World models trained by contrastive learning are a compelling alternative to autoencoder-based world models, which learn by reconstructing pixel states. In this paper, we describe…
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Elise van der Pol, Daniel E. Worrall, Herke van Hoof +2
This paper introduces MDP homomorphic networks for deep reinforcement learning. MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint stat…
Plannable Approximations to MDP Homomorphisms: Equivariance under Actions
Elise van der Pol, Thomas Kipf, Frans A. Oliehoek +1
This work exploits action equivariance for representation learning in reinforcement learning. Equivariance under actions states that transitions in the input space are mirrored by…
Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from ra…
Visual Rationalizations in Deep Reinforcement Learning for Atari Games
Laurens Weitkamp, Elise van der Pol, Zeynep Akata
Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games.…
Hyperspherical Prototype Networks
Pascal Mettes, Elise van der Pol, Cees G. M. Snoek
This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces. For classification, a common app…