  Since reward functions are hard to specify, recent work has focused on
learning policies from human feedback. However, such approaches are impeded by
the expense of acquiring such feedback. Recent work proposed that agents have
access to a source of information that is effectively free: in any environment
that humans have acted in, the state will already be optimized for human
preferences, and thus an agent can extract information about what humans want
from the state. Such learning is possible in principle, but requires simulating
all possible past trajectories that could have led to the observed state. This
is feasible in gridworlds, but how do we scale it to complex tasks? In this
work, we show that by combining a learned feature encoder with learned inverse
models, we can enable agents to simulate human actions backwards in time to
infer what they must have done. The resulting algorithm is able to reproduce a
specific skill in MuJoCo environments given a single state sampled from the
optimal policy for that skill.
