title: Third Person Imitation Learning
abstract: 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.
authors: Bradly C Stadie Pieter Abbeel Ilya Sutskever
hIndex: Pieter Abbeel 126