The prevalent use of benchmarks in current offline reinforcement learning
(RL) 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, we specify properties of imbalanced
datasets in offline RL, where the state coverage follows a power law
distribution characterized by skewed policies. Theoretically and empirically,
we show that typically offline RL methods based on distributional constraints,
such as conservative Q-learning (CQL), are ineffective in extracting policies
under the imbalanced dataset. Inspired by natural intelligence, we propose a
novel offline RL method that utilizes the augmentation of CQL with a retrieval
process to recall past related experiences, 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,
utilizing the variant of D4RL. Empirical results demonstrate the superiority of
our method over other baselines.

通过在分布式的约束条件如 onservative Q-learning 基础上引入信息检索过程，有效地减轻了失衡数据集所带来的挑战，我们提出了一种新颖的离线强化学习方法，并在不同程度失衡的数据集上的几个任务中评估了其优劣。