2 citations · 5 across the 4 of their papers we have counts for
6 papers
Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds
Cláudia Fonseca Pinhão, Chris Eijgenstein, Iva Gornishka +3
Obstacles on the sidewalk often block the path, limiting passage and resulting in frustration and wasted time, especially for citizens and visitors who use assistive devices (wheel…
Back to Basics: Deep Reinforcement Learning in Traffic Signal Control
Sierk Kanis, Laurens Samson, Daan Bloembergen +1
In this paper we revisit some of the fundamental premises for a reinforcement learning (RL) approach to self-learning traffic lights. We propose RLight, a combination of choices th…
Automatic labelling of urban point clouds using data fusion
Daan Bloembergen, Chris Eijgenstein
In this paper we describe an approach to semi-automatically create a labelled dataset for semantic segmentation of urban street-level point clouds. We use data fusion techniques us…
Robust Temporal Difference Learning for Critical Domains
Richard Klima, Daan Bloembergen, Michael Kaisers +1
We present a new Q-function operator for temporal difference (TD) learning methods that explicitly encodes robustness against significant rare events (SRE) in critical domains. The…
On Rational Delegations in Liquid Democracy
Daan Bloembergen, Davide Grossi, Martin Lackner
Liquid democracy is a proxy voting method where proxies are delegable. We propose and study a game-theoretic model of liquid democracy to address the following question: when is it…
Lenient Multi-Agent Deep Reinforcement Learning
Gregory Palmer, Karl Tuyls, Daan Bloembergen +1
Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks…