Cascade - A sequential ensemble method for continuous control tasks

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.