Self-Paced Absolute Learning Progress as a Regularized Approach to
  Curriculum Learning
Tobias Niehues Ulla Scheler Pascal Klink
The usability of Reinforcement Learning is restricted by the large
computation times it requires. Curriculum Reinforcement Learning speeds up
learning by defining a helpful order in which an agent encounters tasks, i.e.
from simple to hard. Curricula based on Absolute Learning Progress (ALP) have
proven successful in different environments, but waste computation on repeating
already learned behaviour in new tasks. We solve this problem by introducing a
new regularization method based on Self-Paced (Deep) Learning, called
Self-Paced Absolute Learning Progress (SPALP). We evaluate our method in three
different environments. Our method achieves performance comparable to original
ALP in all cases, and reaches it quicker than ALP in two of them. We illustrate
possibilities to further improve the efficiency and performance of SPALP.