  Learning task-agnostic dynamics models in high-dimensional observation spaces
can be challenging for model-based RL agents. We propose a novel way to learn
latent world models by learning to predict sequences of future actions
conditioned on task completion. These task-conditioned models adaptively focus
modeling capacity on task-relevant dynamics, while simultaneously serving as an
effective heuristic for planning with sparse rewards. We evaluate our method on
challenging visual goal completion tasks and show a substantial increase in
performance compared to prior model-free approaches.
