  Reinforcement learning is a promising method to accomplish robotic control
tasks. The task of playing musical instruments is, however, largely unexplored
because it involves the challenge of achieving sequential goals - melodies -
that have a temporal dimension. In this paper, we address robotic musicianship
by introducing a temporal extension to goal-conditioned reinforcement learning:
Time-dependent goals. We demonstrate that these can be used to train a robotic
musician to play the theremin instrument. We train the robotic agent in
simulation and transfer the acquired policy to a real-world robotic
thereminist. Supplemental video: https://youtu.be/jvC9mPzdQN4
