<ul><li>The success of deep reinforcement learning (DRL) relies on the availability and quality of training data, often requiring extensive interactions with specific environments.</li><li>Offline reinforcement learning (RL) provides a solution in real-world scenarios where data collection is costly and risky by utilizing data collected by domain experts to search for a batch-constrained optimal policy.</li><li>Transition Scoring (TS) is introduced as a method to assign scores to transitions based on their similarity to the target domain in mixed datasets, addressing the problem of source-target domain mismatch in offline RL.</li><li>Curriculum Learning-Based Trajectory Valuation (CLTV) effectively leverages transition scores to identify and prioritize high-quality trajectories, enhancing the performance and transferability of policies learned by offline RL algorithms.</li></ul>

Enhancing Offline Reinforcement Learning with Curriculum Learning-Based Trajectory Valuation

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