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 research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety considerations. In this paper%2C we specify properties of imbalanced datasets in offline RL%2C where the state coverage follows a power law distribution characterized by skewed policies. Theoretically and empirically%2C we show that typically offline RL methods based on distributional constraints%2C such as conservative Q-learning (CQL)%2C are ineffective in extracting policies under the imbalanced dataset. Inspired by natural intelligence%2C we propose a novel offline RL method that utilizes the augmentation of CQL with a retrieval process to recall past related experiences%2C effectively alleviating the challenges posed by imbalanced datasets. We evaluate our method on several tasks in the context of imbalanced datasets with varying levels of imbalance%2C utilizing the variant of D4RL. Empirical results demonstrate the superiority of our method over other baselines.?quality=80&w=800)
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