  In this paper we study the problem of learning multi-step dynamics prediction
models (jumpy models) from unlabeled experience and their utility for fast
inference of (high-level) plans in downstream tasks. In particular we propose
to learn a jumpy model alongside a skill embedding space offline, from
previously collected experience for which no labels or reward annotations are
required. We then investigate several options of harnessing those learned
components in combination with model-based planning or model-free reinforcement
learning (RL) to speed up learning on downstream tasks. We conduct a set of
experiments in the RGB-stacking environment, showing that planning with the
learned skills and the associated model can enable zero-shot generalization to
new tasks, and can further speed up training of policies via reinforcement
learning. These experiments demonstrate that jumpy models which incorporate
temporal abstraction can facilitate planning in long-horizon tasks in which
standard dynamics models fail.
