Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long-horizon tasks. To overcome this, we introduce the Hierarchical Diffuser, a simple, fast, yet surprisingly effective planning method combining the advantages of hierarchical and diffusion-based planning. Our model adopts a "jumpy" planning strategy at the higher level, which allows it to have a larger receptive field but at a lower computational cost -- a crucial factor for diffusion-based planning methods, as we have empirically verified. Additionally, the jumpy sub-goals guide our low-level planner, facilitating a fine-tuning stage and further improving our approach's effectiveness. We conducted empirical evaluations on standard offline reinforcement learning benchmarks, demonstrating our method's superior performance and efficiency in terms of training and planning speed compared to the non-hierarchical Diffuser as well as other hierarchical planning methods. Moreover, we explore our model's generalization capability, particularly on how our method improves generalization capabilities on compositional out-of-distribution tasks.

翻译：基于扩散的生成方法已被证明在利用离线数据集建模轨迹方面是有效的。然而，它们常常面临计算挑战，并且在泛化方面表现不佳，特别是在捕捉长时域任务的时序抽象时。为了克服这一问题，我们提出了分层扩散器（Hierarchical Diffuser），这是一种简单、快速且令人惊讶地有效的规划方法，结合了分层规划和基于扩散规划的优势。我们的模型在高层采用"跳跃式"规划策略，使其具有更大的感受野，但计算成本更低——这是基于扩散的规划方法的一个关键因素，我们已通过实验验证。此外，跳跃性子目标引导我们的低层规划器，促进了微调阶段，进一步提升了我们方法的有效性。我们在标准离线强化学习基准上进行了实证评估，证明了我们的方法在训练和规划速度方面相比非分层扩散器以及其他分层规划方法具有更优越的性能和效率。此外，我们探索了模型的泛化能力，特别是我们的方法如何在组合式分布外任务上提升泛化能力。