175 citations · 208 across the 6 of their papers we have counts for
6 papers
Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
Constantin Waubert de Puiseau, Fabian Wolz, Merlin Montag +3
The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML)…
Emergent Language: A Survey and Taxonomy
Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan +4
The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learni…
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…
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…
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…