Training Reinforcement Learning Agents and Humans With
  Difficulty-Conditioned Generators
Sidney Tio Jimmy Ho Pradeep Varakantham
We adapt Parameterized Environment Response Model (PERM), a method for
training both Reinforcement Learning (RL) Agents and human learners in
parameterized environments by directly modeling difficulty and ability.
Inspired by Item Response Theory (IRT), PERM aligns environment difficulty with
individual ability, creating a Zone of Proximal Development-based curriculum.
Remarkably, PERM operates without real-time RL updates and allows for offline
training, ensuring its adaptability across diverse students. We present a
two-stage training process that capitalizes on PERM's adaptability, and
demonstrate its effectiveness in training RL agents and humans in an empirical
study.