  Reinforcement learning (RL) makes it possible to train agents capable of
achieving sophisticated goals in complex and uncertain environments. A key
difficulty in reinforcement learning is specifying a reward function for the
agent to optimize. Traditionally, imitation learning in RL has been used to
overcome this problem. Unfortunately, hitherto imitation learning methods tend
to require that demonstrations are supplied in the first-person: the agent is
provided with a sequence of states and a specification of the actions that it
should have taken. While powerful, this kind of imitation learning is limited
by the relatively hard problem of collecting first-person demonstrations.
Humans address this problem by learning from third-person demonstrations: they
observe other humans perform tasks, infer the task, and accomplish the same
task themselves.
  In this paper, we present a method for unsupervised third-person imitation
learning. Here third-person refers to training an agent to correctly achieve a
simple goal in a simple environment when it is provided a demonstration of a
teacher achieving the same goal but from a different viewpoint; and
unsupervised refers to the fact that the agent receives only these third-person
demonstrations, and is not provided a correspondence between teacher states and
student states. Our methods primary insight is that recent advances from domain
confusion can be utilized to yield domain agnostic features which are crucial
during the training process. To validate our approach, we report successful
experiments on learning from third-person demonstrations in a pointmass domain,
a reacher domain, and inverted pendulum.
