Herke van Hoof

DeepAI

Featured Co-authors

Yang Yang
202 publications
Masashi Sugiyama
201 publications
Max Welling
160 publications
Aaron Courville
148 publications
Jan Peters
134 publications
Doina Precup
132 publications
Joelle Pineau
116 publications
Qi Wang
103 publications
Efstratios Gavves
56 publications
Ole-Christoffer Granmo
45 publications
Gerhard Neumann
40 publications

research

∙ 12/22/2022

Reusable Options through Gradient-based Meta Learning

Hierarchical methods in reinforcement learning have the potential to red...

David Kuric, et al. ∙

research

∙ 09/04/2022

Exposure-Aware Recommendation using Contextual Bandits

Exposure bias is a well-known issue in recommender systems where items a...

Masoud Mansoury, et al. ∙

research

∙ 08/20/2022

Calculus on MDPs: Potential Shaping as a Gradient

In reinforcement learning, different reward functions can be equivalent ...

Erik Jenner, et al. ∙

research

∙ 07/13/2022

Neural Topological Ordering for Computation Graphs

Recent works on machine learning for combinatorial optimization have sho...

Mukul Gagrani, et al. ∙

research

∙ 03/08/2022

Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine

Hex is a turn-based two-player connection game with a high branching fac...

Charul Giri, et al. ∙

research

∙ 03/07/2022

Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment

We consider multi-agent reinforcement learning (MARL) for cooperative co...

Tessa van der Heiden, et al. ∙

research

∙ 03/07/2022

Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation

We present Nonparametric Approximation of Inter-Trace returns (NAIT), a ...

Alexander Long, et al. ∙

research

∙ 01/28/2022

Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

Enabling reinforcement learning (RL) agents to leverage a knowledge base...

Niklas Hopner, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 09/01/2021

A Survey of Exploration Methods in Reinforcement Learning

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

Susan Amin, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 12/04/2018

Deep Generative Modeling of LiDAR Data

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

Lucas Caccia, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 11/10/2016

Policy Search with High-Dimensional Context Variables

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

Voot Tangkaratt, et al. ∙