  Offline goal-conditioned reinforcement learning (GCRL) promises
general-purpose skill learning in the form of reaching diverse goals from
purely offline datasets. We propose $\textbf{Go}$al-conditioned
$f$-$\textbf{A}$dvantage $\textbf{R}$egression (GoFAR), a novel
regression-based offline GCRL algorithm derived from a state-occupancy matching
perspective; the key intuition is that the goal-reaching task can be formulated
as a state-occupancy matching problem between a dynamics-abiding imitator agent
and an expert agent that directly teleports to the goal. In contrast to prior
approaches, GoFAR does not require any hindsight relabeling and enjoys
uninterleaved optimization for its value and policy networks. These distinct
features confer GoFAR with much better offline performance and stability as
well as statistical performance guarantee that is unattainable for prior
methods. Furthermore, we demonstrate that GoFAR's training objectives can be
re-purposed to learn an agent-independent goal-conditioned planner from purely
offline source-domain data, which enables zero-shot transfer to new target
domains. Through extensive experiments, we validate GoFAR's effectiveness in
various problem settings and tasks, significantly outperforming prior
state-of-art. Notably, on a real robotic dexterous manipulation task, while no
other method makes meaningful progress, GoFAR acquires complex manipulation
behavior that successfully accomplishes diverse goals.
