From the 1 of 7 linked papers with an AI index.
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Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Mingxuan Che, Tsung-Yuan Tseng, Theresa Eimer +2
The paper introduces ERAHBO, a Bayesian optimization approach that models both the mean and variance of reinforcement learning performance with respect to hyperparameters, aiming t…
Beyond Success Rates: Trainability and Extractability for Offline GCRL
Jan Malte Töpperwien, Aditya Mohan, Marius Lindauer
Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method. This score measures attainable performance, but it do…
Moments Matter:Stabilizing Policy Optimization using Return Distributions
Dennis Jabs, Aditya Mohan, Marius Lindauer
Deep Reinforcement Learning (RL) agents often learn policies that achieve the same episodic return yet behave very differently, due to a combination of environmental (random transi…
Revisiting Learning Rate Control
Micha Henheik, Theresa Eimer, Marius Lindauer
The learning rate is one of the most important hyperparameters in deep learning, and how to control it is an active area within both AutoML and deep learning research. Approaches f…
Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning
Lukas Fehring, Marius Lindauer, Theresa Eimer
While increasingly large models have revolutionized much of the machine learning landscape, training even mid-sized networks for Reinforcement Learning (RL) is still proving to be…
AutoML for Multi-Class Anomaly Compensation of Sensor Drift
Melanie Schaller, Mathis Kruse, Antonio Ortega +2
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as…