91 citations · 129 across the 16 of their papers we have counts for
14 papers · 1 filter
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Mingxuan Che, Tsung-Yuan Tseng, Theresa Eimer +2
Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both expected performance and vari…
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…
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…
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…
Hyperparameters in Reinforcement Learning and How To Tune Them
Theresa Eimer, Marius Lindauer, Roberta Raileanu
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting. However,…
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Alexander Tornede, Difan Deng, Theresa Eimer +8
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language…
Hyperparameters in Contextual RL are Highly Situational
Theresa Eimer, Carolin Benjamins, Marius Lindauer
Although Reinforcement Learning (RL) has shown impressive results in games and simulation, real-world application of RL suffers from its instability under changing environment cond…
Contextualize Me -- The Case for Context in Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +6
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.…
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Jack Parker-Holder, Raghu Rajan, Xingyou Song +9
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable a…
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +4
While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment. This…
Automatic Risk Adaptation in Distributional Reinforcement Learning
Frederik Schubert, Theresa Eimer, Bodo Rosenhahn +1
The use of Reinforcement Learning (RL) agents in practical applications requires the consideration of suboptimal outcomes, depending on the familiarity of the agent with its enviro…
Self-Paced Context Evaluation for Contextual Reinforcement Learning
Theresa Eimer, André Biedenkapp, Frank Hutter +1
Reinforcement learning (RL) has made a lot of advances for solving a single problem in a given environment; but learning policies that generalize to unseen variations of a problem…