  A major challenge in the Deep RL (DRL) community is to train agents able to
generalize over unseen situations, which is often approached by training them
on a diversity of tasks (or environments). A powerful method to foster
diversity is to procedurally generate tasks by sampling their parameters from a
multi-dimensional distribution, enabling in particular to propose a different
task for each training episode. In practice, to get the high diversity of
training tasks necessary for generalization, one has to use complex procedural
generation systems. With such generators, it is hard to get prior knowledge on
the subset of tasks that are actually learnable at all (many generated tasks
may be unlearnable), what is their relative difficulty and what is the most
efficient task distribution ordering for training. A typical solution in such
cases is to rely on some form of Automated Curriculum Learning (ACL) to adapt
the sampling distribution. One limit of current approaches is their need to
explore the task space to detect progress niches over time, which leads to a
loss of time. Additionally, we hypothesize that the induced noise in the
training data may impair the performances of brittle DRL learners. We address
this problem by proposing a two stage ACL approach where 1) a teacher algorithm
first learns to train a DRL agent with a high-exploration curriculum, and then
2) distills learned priors from the first run to generate an "expert
curriculum" to re-train the same agent from scratch. Besides demonstrating 50%
improvements on average over the current state of the art, the objective of
this work is to give a first example of a new research direction oriented
towards refining ACL techniques over multiple learners, which we call Classroom
Teaching.
