  Planning with generative models has emerged as an effective decision-making
paradigm across a wide range of domains, including reinforcement learning and
autonomous navigation. While continuous replanning at each timestep might seem
intuitive because it allows decisions to be made based on the most recent
environmental observations, it results in substantial computational challenges,
primarily due to the complexity of the generative model's underlying deep
learning architecture. Our work addresses this challenge by introducing a
simple adaptive planning policy that leverages the generative model's ability
to predict long-horizon state trajectories, enabling the execution of multiple
actions consecutively without the need for immediate replanning. We propose to
use the predictive uncertainty derived from a Deep Ensemble of inverse dynamics
models to dynamically adjust the intervals between planning sessions. In our
experiments conducted on locomotion tasks within the OpenAI Gym framework, we
demonstrate that our adaptive planning policy allows for a reduction in
replanning frequency to only about 10% of the steps without compromising the
performance. Our results underscore the potential of generative modeling as an
efficient and effective tool for decision-making.
