1 code implementation • 20 Nov 2017 • Gino Brunner, Oliver Richter, Yuyi Wang, Roger Wattenhofer
Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community.
2 code implementations • 19 May 2020 • Oliver Richter, Roger Wattenhofer
Attention architectures are widely used; they recently gained renewed popularity with Transformers yielding a streak of state of the art results.
1 code implementation • 5 Jul 2019 • Timo Bram, Gino Brunner, Oliver Richter, Roger Wattenhofer
Sharing knowledge between tasks is vital for efficient learning in a multi-task setting.
1 code implementation • 30 Sep 2018 • Gino Brunner, Manuel Fritsche, Oliver Richter, Roger Wattenhofer
Learning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards.
1 code implementation • 20 Nov 2022 • Jeremia Geiger, Karolis Martinkus, Oliver Richter, Roger Wattenhofer
Rigid origami has shown potential in large diversity of practical applications.
no code implementations • 27 Jun 2019 • Oliver Richter, Roger Wattenhofer
Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space control setting.
no code implementations • ICLR 2020 • Gino Brunner, Yang Liu, Damián Pascual, Oliver Richter, Massimiliano Ciaramita, Roger Wattenhofer
We show that, for sequences longer than the attention head dimension, attention weights are not identifiable.
no code implementations • 25 Sep 2019 • Julian Zilly, Hannes Zilly, Oliver Richter, Roger Wattenhofer, Andrea Censi, Emilio Frazzoli
Empirically across several data domains, we substantiate this viewpoint by showing that test performance correlates strongly with the distance in data distributions between training and test set.