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…