Rethinking Teacher-Student Curriculum Learning through the Cooperative
  Mechanics of Experience
Manfred Diaz Liam Paull Andrea Tacchetti
Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework
that draws inspiration from human cultural transmission and learning. It
involves a teacher algorithm shaping the learning process of a learner
algorithm by exposing it to controlled experiences. Despite its success,
understanding the conditions under which TSCL is effective remains challenging.
In this paper, we propose a data-centric perspective to analyze the underlying
mechanics of the teacher-student interactions in TSCL. We leverage cooperative
game theory to describe how the composition of the set of experiences presented
by the teacher to the learner, as well as their order, influences the
performance of the curriculum that is found by TSCL approaches. To do so, we
demonstrate that for every TSCL problem, there exists an equivalent cooperative
game, and several key components of the TSCL framework can be reinterpreted
using game-theoretic principles. Through experiments covering supervised
learning, reinforcement learning, and classical games, we estimate the
cooperative values of experiences and use value-proportional curriculum
mechanisms to construct curricula, even in cases where TSCL struggles. The
framework and experimental setup we present in this work represent a novel
foundation for a deeper exploration of TSCL, shedding light on its underlying
mechanisms and providing insights into its broader applicability in machine
learning.