  Recently, various successful applications utilizing expert states in
imitation learning (IL) have been witnessed. However, another IL setting -- IL
from visual inputs (ILfVI), which has a greater promise to be applied in
reality by utilizing online visual resources, suffers from low data-efficiency
and poor performance resulted from an on-policy learning manner and
high-dimensional visual inputs. We propose OPIfVI (Off-Policy Imitation from
Visual Inputs), which is composed of an off-policy learning manner, data
augmentation, and encoder techniques, to tackle the mentioned challenges,
respectively. More specifically, to improve data-efficiency, OPIfVI conducts IL
in an off-policy manner, with which sampled data can be used multiple times. In
addition, we enhance the stability of OPIfVI with spectral normalization to
mitigate the side-effect of off-policy training. The core factor, contributing
to the poor performance of ILfVI, that we think is the agent could not extract
meaningful features from visual inputs. Hence, OPIfVI employs data augmentation
from computer vision to help train encoders that can better extract features
from visual inputs. In addition, a specific structure of gradient
backpropagation for the encoder is designed to stabilize the encoder training.
At last, we demonstrate that OPIfVI is able to achieve expert-level performance
and outperform existing baselines no matter visual demonstrations or visual
observations are provided via extensive experiments using DeepMind Control
Suite.

Machine Learning;Computer Vision and Pattern Recognition