  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.
