49 citations · 131 across the 37 of their papers we have counts for
18 papers · 1 filter
Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis
Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are avai…
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…
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
Simon Hakenes, Tobias Glasmachers
This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to o…
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes
Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1
To gain a deeper understanding of the behavior and learning dynamics of (deep) artificial neural networks, it is valuable to employ mathematical abstractions and models. These tool…
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…
Invariance to Quantile Selection in Distributional Continuous Control
Felix Grün, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1
In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action do…