  Model-based reinforcement learning methods often use learning only for the
purpose of estimating an approximate dynamics model, offloading the rest of the
decision-making work to classical trajectory optimizers. While conceptually
simple, this combination has a number of empirical shortcomings, suggesting
that learned models may not be well-suited to standard trajectory optimization.
In this paper, we consider what it would look like to fold as much of the
trajectory optimization pipeline as possible into the modeling problem, such
that sampling from the model and planning with it become nearly identical. The
core of our technical approach lies in a diffusion probabilistic model that
plans by iteratively denoising trajectories. We show how classifier-guided
sampling and image inpainting can be reinterpreted as coherent planning
strategies, explore the unusual and useful properties of diffusion-based
planning methods, and demonstrate the effectiveness of our framework in control
settings that emphasize long-horizon decision-making and test-time flexibility.
