  Passive observational data, such as human videos, is abundant and rich in
information, yet remains largely untapped by current RL methods. Perhaps
surprisingly, we show that passive data, despite not having reward or action
labels, can still be used to learn features that accelerate downstream RL. Our
approach learns from passive data by modeling intentions: measuring how the
likelihood of future outcomes change when the agent acts to achieve a
particular task. We propose a temporal difference learning objective to learn
about intentions, resulting in an algorithm similar to conventional RL, but
which learns entirely from passive data. When optimizing this objective, our
agent simultaneously learns representations of states, of policies, and of
possible outcomes in an environment, all from raw observational data. Both
theoretically and empirically, this scheme learns features amenable for value
prediction for downstream tasks, and our experiments demonstrate the ability to
learn from many forms of passive data, including cross-embodiment video data
and YouTube videos.
