21 citations · 68 across the 11 of their papers we have counts for
11 papers
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes…
Active learning for reducing labeling effort in text classification tasks
Pieter Floris Jacobs, Gideon Maillette de Buy Wenniger, Marco Wiering +1
Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Ac…
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows age…
Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control
Arthur Müller, Vishal Rangras, Georg Schnittker +7
Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforceme…
Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification
Xiangxie Zhang, Ben Beinke, Berlian Al Kindhi +1
The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, thre…
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control
Hamid Radmard Rahmani, Carsten Koenke, Marco A. Wiering
In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives…
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO
Mario S. Holubar, Marco A. Wiering
In this paper, a novel racing environment for OpenAI Gym is introduced. This environment operates with continuous action- and state-spaces and requires agents to learn to control t…
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly…
Deep Quality-Value (DQV) Learning
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a Value neural network and…
Sampled Policy Gradient for Learning to Play the Game Agar.io
Anton Orell Wiehe, Nil Stolt Ansó, Madalina M. Drugan +1
In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradi…
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Mathijs Pieters, Marco Wiering
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervise…