UCB exploration via Q-ensembles in RL framework

AI Impact Summary

The change enables upper-confidence-bound exploration using an ensemble of Q-value estimators to drive action selection, signaling a shift to uncertainty-aware RL. This affects how agents compute Q-values and decide actions during training and deployment; teams must implement UCB across the ensemble, determine an appropriate ensemble size, and ensure proper calibration. The added compute and memory load from multiple Q-networks may raise training costs and latency, but can improve sample efficiency in uncertain or sparse environments.