  Curriculum learning is often employed in deep reinforcement learning to let
the agent progress more quickly towards better behaviors. Numerical methods for
curriculum learning in the literature provides only initial heuristic
solutions, with little to no guarantee on their quality. We define a new
gray-box function that, including a suitable scheduling problem, can be
effectively used to reformulate the curriculum learning problem. We propose
different efficient numerical methods to address this gray-box reformulation.
Preliminary numerical results on a benchmark task in the curriculum learning
literature show the viability of the proposed approach.
