  Humans often learn how to perform tasks via imitation: they observe others
perform a task, and then very quickly infer the appropriate actions to take
based on their observations. While extending this paradigm to autonomous agents
is a well-studied problem in general, there are two particular aspects that
have largely been overlooked: (1) that the learning is done from observation
only (i.e., without explicit action information), and (2) that the learning is
typically done very quickly. In this work, we propose a two-phase, autonomous
imitation learning technique called behavioral cloning from observation (BCO),
that aims to provide improved performance with respect to both of these
aspects. First, we allow the agent to acquire experience in a self-supervised
fashion. This experience is used to develop a model which is then utilized to
learn a particular task by observing an expert perform that task without the
knowledge of the specific actions taken. We experimentally compare BCO to
imitation learning methods, including the state-of-the-art, generative
adversarial imitation learning (GAIL) technique, and we show comparable task
performance in several different simulation domains while exhibiting increased
learning speed after expert trajectories become available.
