175 citations · 214 across the 9 of their papers we have counts for
9 papers
Curriculum Learning in Job Shop Scheduling using Reinforcement Learning
Constantin Waubert de Puiseau, Hasan Tercan, Tobias Meisen
Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, neverthel…
schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
Constantin Waubert de Puiseau, Jannik Peters, Christian Dörpelkus +2
Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing schedu…
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
Christian Bitter, Timo Thun, Tobias Meisen
In reinforcement learning (RL) research, simulations enable benchmarks between algorithms, as well as prototyping and hyper-parameter tuning of agents. In order to promote RL both…
Towards NLP-supported Semantic Data Management
Andreas Burgdorf, André Pomp, Tobias Meisen
The heterogeneity of data poses a great challenge when data from different sources is to be merged for one application. Solutions for this are offered, for example, by ontology-bas…
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
Richard Meyes, Constantin Waubert de Puiseau, Andres Posada-Moreno +1
The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically chall…
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents
Richard Meyes, Moritz Schneider, Tobias Meisen
The demand for more transparency of decision-making processes of deep reinforcement learning agents is greater than ever, due to their increased use in safety critical and ethicall…
Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces
Andrei Ionita, André Pomp, Michael Cochez +2
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promisi…
Ablation Studies in Artificial Neural Networks
Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau +1
Ablation studies have been widely used in the field of neuroscience to tackle complex biological systems such as the extensively studied Drosophila central nervous system, the vert…
Ablation of a Robot's Brain: Neural Networks Under a Knife
Peter E. Lillian, Richard Meyes, Tobias Meisen
It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward. We present a novel method for determ…