Reinforcement Learning Journal, vol. TBD, 2025, pp. TBD.
Presented at the Reinforcement Learning Conference (RLC), Edmonton, Alberta, Canada, August 5–9, 2025.
Though reinforcement learning has been successfully applied to a variety of domains, there is still room left for improvement, in particular, in terms of the final performance. Ensemble Reinforcement Learning (ERL) tries to enhance reinforcement learning techniques by using multiple models or algorithms. We propose a novel ERL technique, called Cascade which in the context of continuous control tasks and with PPO as the base training algorithm clearly outperforms standard PPO in terms of the final performance. To shine light on the working mechanisms of Cascade, we conduct ablation studies, showing how the different components of Cascade contribute to its overall performance. Furthermore, we demonstrate that Cascade has a robust monotonicity as the ensemble’s performance increases with each additional base agent even when weak base agents are added in large numbers.
Robin Schmöcker and Alexander Dockhorn. "Cascade - A sequential ensemble method for continuous control tasks." Reinforcement Learning Journal, vol. TBD, 2025, pp. TBD.
BibTeX:@article{schmocker2025cascade,
title={Cascade - {A} sequential ensemble method for continuous control tasks},
author={Schm{\"{o}}cker, Robin and Dockhorn, Alexander},
journal={Reinforcement Learning Journal},
year={2025}
}