  Reinforcement learning requires manual specification of a reward function to
learn a task. While in principle this reward function only needs to specify the
task goal, in practice reinforcement learning can be very time-consuming or
even infeasible unless the reward function is shaped so as to provide a smooth
gradient towards a successful outcome. This shaping is difficult to specify by
hand, particularly when the task is learned from raw observations, such as
images. In this paper, we study how we can automatically learn dynamical
distances: a measure of the expected number of time steps to reach a given goal
state from any other state. These dynamical distances can be used to provide
well-shaped reward functions for reaching new goals, making it possible to
learn complex tasks efficiently. We show that dynamical distances can be used
in a semi-supervised regime, where unsupervised interaction with the
environment is used to learn the dynamical distances, while a small amount of
preference supervision is used to determine the task goal, without any manually
engineered reward function or goal examples. We evaluate our method both on a
real-world robot and in simulation. We show that our method can learn to turn a
valve with a real-world 9-DoF hand, using raw image observations and just ten
preference labels, without any other supervision. Videos of the learned skills
can be found on the project website:
https://sites.google.com/view/dynamical-distance-learning.
