Perceptual Reward Functions
  Reinforcement learning problems are often described through rewards that
indicate if an agent has completed some task. This specification can yield
desirable behavior, however many problems are difficult to specify in this
manner, as one often needs to know the proper configuration for the agent. When
humans are learning to solve tasks, we often learn from visual instructions
composed of images or videos. Such representations motivate our development of
Perceptual Reward Functions, which provide a mechanism for creating visual task
descriptions. We show that this approach allows an agent to learn from rewards
that are based on raw pixels rather than internal parameters.
