From the 1 of 8 linked papers with an AI index.
6 papers · 1 filter
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…
Learning to Play Blackjack: A Curriculum Learning Perspective
Amirreza Alasti, Efe Erdal, Yücel Celik +1
Reinforcement Learning (RL) agents often struggle with efficiency and performance in complex environments. We propose a novel framework that uses a Large Language Model (LLM) to dy…
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Jannis Becktepe, Julian Dierkes, Carolin Benjamins +7
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tun…
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…
Task Scheduling & Forgetting in Multi-Task Reinforcement Learning
Marc Speckmann, Theresa Eimer
Reinforcement learning (RL) agents can forget tasks they have previously been trained on. There is a rich body of work on such forgetting effects in humans. Therefore we look for c…