What is ensemble learning for reinforcement learning systems?

Ensemble reinforcement learning combines multiple policies or value functions to improve stability and exploration.

In reinforcement learning, ensembles are used to reduce variance in value estimation and improve exploration. Multiple Q-networks or policies are trained with different initializations or replay buffers. Their outputs are aggregated to reduce overestimation bias. This improves stability in environments with high uncertainty and sparse rewards.