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RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking
Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key challenge, however, is learning an accurate critic in large state--action spaces with limited dataset coverage. To mitigate harmful updates from value over
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