Mohammed B. Mohiuddin

Fuzzy Ensembles of Reinforcement Learning Policies for Systems With Variable Parameters

This paper presents a novel approach to improving the generalization capabilities of reinforcement learning (RL) agents for robotic systems with varying physical parameters. We propose the Fuzzy Ensemble of RL policies (FERL), which enhances performance in environments where system parameters differ