  For an autonomous agent to fulfill a wide range of user-specified goals at
test time, it must be able to learn broadly applicable and general-purpose
skill repertoires. Furthermore, to provide the requisite level of generality,
these skills must handle raw sensory input such as images. In this paper, we
propose an algorithm that acquires such general-purpose skills by combining
unsupervised representation learning and reinforcement learning of
goal-conditioned policies. Since the particular goals that might be required at
test-time are not known in advance, the agent performs a self-supervised
"practice" phase where it imagines goals and attempts to achieve them. We learn
a visual representation with three distinct purposes: sampling goals for
self-supervised practice, providing a structured transformation of raw sensory
inputs, and computing a reward signal for goal reaching. We also propose a
retroactive goal relabeling scheme to further improve the sample-efficiency of
our method. Our off-policy algorithm is efficient enough to learn policies that
operate on raw image observations and goals for a real-world robotic system,
and substantially outperforms prior techniques.
