39 citations · 140 across the 12 of their papers we have counts for
4 papers · 1 filter
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Tim Pearce, Felix Leibfried, Alexandra Brintrup +2
Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying…
Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks
Felix Leibfried, Peter Vrancx
This paper proposes a new optimization objective for value-based deep reinforcement learning. We extend conventional Deep Q-Networks (DQNs) by adding a model-learning component yie…
An information-theoretic on-line update principle for perception-action coupling
Zhen Peng, Tim Genewein, Felix Leibfried +1
Inspired by findings of sensorimotor coupling in humans and animals, there has recently been a growing interest in the interaction between action and perception in robotic systems…
Balancing Two-Player Stochastic Games with Soft Q-Learning
Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision m…