<ul data-eligibleForWebStory="true"><li>Offline RL addresses challenges in DRL by learning from pre-collected datasets.</li><li>MOORL is a hybrid framework combining offline and online RL for efficient learning.</li><li>Meta Offline-Online RL utilizes a meta-policy to adapt across offline and online trajectories.</li><li>MOORL improves exploration while leveraging offline data for robust initialization.</li><li>The hybrid approach enhances exploration by combining strengths of offline and online data.</li><li>MOORL achieves stable Q-function learning without added complexity.</li><li>Experiments on 28 tasks validate MOORL's effectiveness over existing baselines.</li><li>MOORL shows consistent improvements in performance.</li><li>The framework has potential for practical applications with minimal computational overhead.</li></ul>

MOORL: A Framework for Integrating Offline-Online Reinforcement Learning

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