Cross-Domain Perceptual Reward Functions
  In reinforcement learning, we often define goals by specifying rewards within
desirable states. One problem with this approach is that we typically need to
redefine the rewards each time the goal changes, which often requires some
understanding of the solution in the agents environment. When humans are
learning to complete tasks, we regularly utilize alternative sources that guide
our understanding of the problem. Such task representations allow one to
specify goals on their own terms, thus providing specifications that can be
appropriately interpreted across various environments. This motivates our own
work, in which we represent goals in environments that are different from the
agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned
rewards that represent the visual similarity between an agents state and a
cross-domain goal image. We report results for learning the CDPRs with a deep
neural network and using them to solve two tasks with deep reinforcement
learning.
