Herke van Hoof

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University of Amsterdam

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∙ 10/09/2021

Multi-Agent MDP Homomorphic Networks

This paper introduces Multi-Agent MDP Homomorphic Networks, a class of n...

Elise van der Pol, et al. ∙

∙ 09/23/2021

Hierarchies of Planning and Reinforcement Learning for Robot Navigation

Solving robotic navigation tasks via reinforcement learning (RL) is chal...

Jan Wöhlke, et al. ∙

∙ 09/01/2021

A Survey of Exploration Methods in Reinforcement Learning

Exploration is an essential component of reinforcement learning algorith...

Susan Amin, et al. ∙

∙ 03/22/2021

Combining Reward Information from Multiple Sources

Given two sources of evidence about a latent variable, one can combine t...

Dmitrii Krasheninnikov, et al. ∙

∙ 02/23/2021

Deep Policy Dynamic Programming for Vehicle Routing Problems

Routing problems are a class of combinatorial problems with many practic...

Wouter Kool, et al. ∙

∙ 02/16/2021

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models

Reinforcement learning is a promising paradigm for solving sequential de...

Qi Wang, et al. ∙

∙ 10/30/2020

Experimental design for MRI by greedy policy search

In today's clinical practice, magnetic resonance imaging (MRI) is routin...

Tim Bakker, et al. ∙

∙ 08/21/2020

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Neural processes (NPs) constitute a family of variational approximate mo...

Qi Wang, et al. ∙

∙ 07/03/2020

An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques

Air traffic control is becoming a more and more complex task due to the ...

Joris Mollinga, et al. ∙

∙ 06/30/2020

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

This paper introduces MDP homomorphic networks for deep reinforcement le...

Elise van der Pol, et al. ∙

∙ 03/18/2020

Social navigation with human empowerment driven reinforcement learning

The next generation of mobile robots needs to be socially-compliant to b...

Tessa van der Heiden, et al. ∙

∙ 02/14/2020

Estimating Gradients for Discrete Random Variables by Sampling without Replacement

We derive an unbiased estimator for expectations over discrete random va...

Wouter Kool, et al. ∙

∙ 10/23/2019

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Neural Network based controllers hold enormous potential to learn comple...

Sanjay Thakur, et al. ∙

∙ 06/15/2019

Reinforcement Learning with Non-uniform State Representations for Adaptive Search

Efficient spatial exploration is a key aspect of search and rescue. In t...

Sandeep Manjanna, et al. ∙

∙ 03/14/2019

Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

The well-known Gumbel-Max trick for sampling from a categorical distribu...

Wouter Kool, et al. ∙

∙ 03/13/2019

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Diversity of environments is a key challenge that causes learned robotic...

Sanjay Thakur, et al. ∙

∙ 12/04/2018

Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challen...

Lucas Caccia, et al. ∙

∙ 09/25/2018

BanditSum: Extractive Summarization as a Contextual Bandit

In this work, we propose a novel method for training neural networks to ...

Yue Dong, et al. ∙

∙ 02/26/2018

Addressing Function Approximation Error in Actor-Critic Methods

In value-based reinforcement learning methods such as deep Q-learning, f...

Scott Fujimoto, et al. ∙

∙ 11/10/2016

Policy Search with High-Dimensional Context Variables

Direct contextual policy search methods learn to improve policy paramete...

Voot Tangkaratt, et al. ∙