Temporal abstraction and efficient planning pose significant challenges in offline reinforcement learning, mainly when dealing with domains that involve temporally extended tasks and delayed sparse rewards. Existing methods typically plan in the raw action space and can be inefficient and inflexible. Latent action spaces offer a more flexible paradigm, capturing only possible actions within the behavior policy support and decoupling the temporal structure between planning and modeling. However, current latent-action-based methods are limited to discrete spaces and require expensive planning. This paper presents a unified framework for continuous latent action space representation learning and planning by leveraging latent, score-based diffusion models. We establish the theoretical equivalence between planning in the latent action space and energy-guided sampling with a pretrained diffusion model and incorporate a novel sequence-level exact sampling method. Our proposed method, $\texttt{LatentDiffuser}$, demonstrates competitive performance on low-dimensional locomotion control tasks and surpasses existing methods in higher-dimensional tasks.

该研究论文介绍了一种统一的框架，利用潜在的、基于分数的扩散模型进行连续潜在行动空间的表示学习和规划，通过考虑预训练的扩散模型进行能量引导抽样，通过引入一种新颖的序列级别的精确抽样方法。所提出的方法在低维机动控制任务上表现出竞争性能，并在高维任务中超过了现有的方法。

潜在扩散下的高效规划