Enable UCB exploration with Q-ensembles in RL training workflow

AI Impact Summary

This change introduces an Upper Confidence Bound (UCB) exploration strategy implemented via Q-ensembles in the reinforcement learning training workflow. By leveraging multiple Q-networks to estimate value and uncertainty, the agent should explore more effectively in uncertain states, potentially accelerating convergence and improving policy quality on sparse-reward tasks. Expect higher compute and memory usage due to maintaining ensembles; teams should plan for larger resource budgets and tune ensemble size and the UCB confidence parameter to balance performance gains against cost and training stability.