1 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
Abhijeet Pendyala, Asma Atamna, Tobias Glasmachers
We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput…
ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation
Abhijeet Pendyala, Justin Dettmer, Tobias Glasmachers +1
We present ContainerGym, a benchmark for reinforcement learning inspired by a real-world industrial resource allocation task. The proposed benchmark encodes a range of challenges c…
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning
Riad Akrour, Asma Atamna, Jan Peters
Convex optimizers have known many applications as differentiable layers within deep neural architectures. One application of these convex layers is to project points into a convex…