  Applying end-to-end learning to solve complex, interactive, pixel-driven
control tasks on a robot is an unsolved problem. Deep Reinforcement Learning
algorithms are too slow to achieve performance on a real robot, but their
potential has been demonstrated in simulated environments. We propose using
progressive networks to bridge the reality gap and transfer learned policies
from simulation to the real world. The progressive net approach is a general
framework that enables reuse of everything from low-level visual features to
high-level policies for transfer to new tasks, enabling a compositional, yet
simple, approach to building complex skills. We present an early demonstration
of this approach with a number of experiments in the domain of robot
manipulation that focus on bridging the reality gap. Unlike other proposed
approaches, our real-world experiments demonstrate successful task learning
from raw visual input on a fully actuated robot manipulator. Moreover, rather
than relying on model-based trajectory optimisation, the task learning is
accomplished using only deep reinforcement learning and sparse rewards.
