  We present a method for learning intrinsic reward functions to drive the
learning of an agent during periods of practice in which extrinsic task rewards
are not available. During practice, the environment may differ from the one
available for training and evaluation with extrinsic rewards. We refer to this
setup of alternating periods of practice and objective evaluation as
practice-match, drawing an analogy to regimes of skill acquisition common for
humans in sports and games. The agent must effectively use periods in the
practice environment so that performance improves during matches. In the
proposed method the intrinsic practice reward is learned through a
meta-gradient approach that adapts the practice reward parameters to reduce the
extrinsic match reward loss computed from matches. We illustrate the method on
a simple grid world, and evaluate it in two games in which the practice
environment differs from match: Pong with practice against a wall without an
opponent, and PacMan with practice in a maze without ghosts. The results show
gains from learning in practice in addition to match periods over learning in
matches only.

Artificial Intelligence