3 papers
cs.LG2026
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…
cs.NE2025
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Roman Kalkreuth, Fabricio Olivetti de França, Julian Dierkes +4
Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, i…
cs.LG2024
Combining Automated Optimisation of Hyperparameters and Reward Shape
Julian Dierkes, Emma Cramer, Holger H. Hoos +1
There has been significant progress in deep reinforcement learning (RL) in recent years. Nevertheless, finding suitable hyperparameter configurations and reward functions remains c…