  Unsupervised pre-training has recently become the bedrock for computer vision
and natural language processing. In reinforcement learning (RL),
goal-conditioned RL can potentially provide an analogous self-supervised
approach for making use of large quantities of unlabeled (reward-free) data.
However, building effective algorithms for goal-conditioned RL that can learn
directly from diverse offline data is challenging, because it is hard to
accurately estimate the exact value function for faraway goals. Nonetheless,
goal-reaching problems exhibit structure, such that reaching distant goals
entails first passing through closer subgoals. This structure can be very
useful, as assessing the quality of actions for nearby goals is typically
easier than for more distant goals. Based on this idea, we propose a
hierarchical algorithm for goal-conditioned RL from offline data. Using one
action-free value function, we learn two policies that allow us to exploit this
structure: a high-level policy that treats states as actions and predicts (a
latent representation of) a subgoal and a low-level policy that predicts the
action for reaching this subgoal. Through analysis and didactic examples, we
show how this hierarchical decomposition makes our method robust to noise in
the estimated value function. We then apply our method to offline goal-reaching
benchmarks, showing that our method can solve long-horizon tasks that stymie
prior methods, can scale to high-dimensional image observations, and can
readily make use of action-free data. Our code is available at
https://seohong.me/projects/hiql/
