Wei Xu

RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking

RankQ, an offline-to-online Q-learning objective that augments temporal-difference learning with a self-supervised multi-term ranking loss to enforce structured action ordering is proposed, which shapes the Q-function such that action gradients are directed toward higher-quality behaviors.