  Reinforcement learning (RL) presents a promising framework to learn policies
through environment interaction, but often requires an infeasible amount of
interaction data to solve complex tasks from sparse rewards. One direction
includes augmenting RL with offline data demonstrating desired tasks, but past
work often require a lot of high-quality demonstration data that is difficult
to obtain, especially for domains such as robotics. Our approach consists of a
reverse curriculum followed by a forward curriculum. Unique to our approach
compared to past work is the ability to efficiently leverage more than one
demonstration via a per-demonstration reverse curriculum generated via state
resets. The result of our reverse curriculum is an initial policy that performs
well on a narrow initial state distribution and helps overcome difficult
exploration problems. A forward curriculum is then used to accelerate the
training of the initial policy to perform well on the full initial state
distribution of the task and improve demonstration and sample efficiency. We
show how the combination of a reverse curriculum and forward curriculum in our
method, RFCL, enables significant improvements in demonstration and sample
efficiency compared against various state-of-the-art
learning-from-demonstration baselines, even solving previously unsolvable tasks
that require high precision and control.
