  The growing use of virtual autonomous agents in applications like games and
entertainment demands better control policies for natural-looking movements and
actions. Unlike the conventional approach of hard-coding motion routines, we
propose a deep learning method for obtaining control policies by directly
mimicking raw video demonstrations. Previous methods in this domain rely on
extracting low-dimensional features from expert videos followed by a separate
hand-crafted reward estimation step. We propose an imitation learning framework
that reduces the dependence on hand-engineered reward functions by jointly
learning the feature extraction and reward estimation steps using Generative
Adversarial Networks (GANs). Our main contribution in this paper is to show
that under injective mapping between low-level joint state (angles and
velocities) trajectories and corresponding raw video stream, performing
adversarial imitation learning on video demonstrations is equivalent to
learning from the state trajectories. Experimental results show that the
proposed adversarial learning method from raw videos produces a similar
performance to state-of-the-art imitation learning techniques while frequently
outperforming existing hand-crafted video imitation methods. Furthermore, we
show that our method can learn action policies by imitating video
demonstrations on YouTube with similar performance to learned agents from true
reward signals. Please see the supplementary video submission at
https://ibm.biz/BdzzNA.
