Inspired by dataset distillation (DD) [Wang et al., 2018, Zhao et al., (Corollary 1). Extensive experiments on nine datasets of D4RL benchmark [Fu et al., 2020] with multiple environments and data qualities illustrate that our Av-PBC remarkably promotes the OBD performance, Moreover, Av-PBC has a significant convergence speed and requires only a quarter of distillation steps compared to DBC and PBC.

This phenomenon cannot be easily explained by offline RL's return maximization objective.

Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions.

Offline Reinforcement Learning (RL) aims to address the challenge of distribution shift between the dataset and the learned policy, where the value of out-of-distribution (OOD) data may be erroneously estimated due to overgeneralization.

When the collected dataset is good, i.e., mostly