3 papers
cs.LG2025
Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control
Tom Maus, Asma Atamna, Tobias Glasmachers
Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its…
cs.LG2025
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
Asma Atamna, Tom Maus, Fabian Kievelitz +1
The learning rate is a crucial hyperparameter in deep learning, with its ideal value depending on the problem and potentially changing during training. In this paper, we investigat…
cs.LG2025
Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments
Tom Maus, Asma Atamna, Tobias Glasmachers
Reinforcement Learning (RL) has demonstrated significant potential in certain real-world industrial applications, yet its broader deployment remains limited by inherent challenges…