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cs.LG2021
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…
cs.RO2021
Action Priors for Large Action Spaces in Robotics
Ondrej Biza, Dian Wang, Robert Platt +2
In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a conseque…