Fuzzy Ensembles of Reinforcement Learning Policies for Systems With Variable Parameters

Abstract

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 from those encountered during training. The FERL method selectively fuses aligned policies, determining their collective decision based on fuzzy memberships tailored to the current parameters of the system. Unlike traditional centralized training approaches that rely on shared experiences for policy updates, FERL allows for independent agent training, facilitating efficient parallelization. The effectiveness of FERL is demonstrated through extensive experiments, including a real-world trajectory tracking application in a quadrotor slung-load system. Our method improves the success rates by up to 15.6% across various simulated systems with variable parameters compared to the existing benchmarks of domain randomization and robust adaptive ensemble adversary RL. In the real-world experiments, our method achieves a 30% reduction in 3D position RMSE compared to individual RL policies. The results underscores FERL robustness and applicability to real robotic systems.