  Knowledge distillation between machine learning models has opened many new
avenues for parameter count reduction, performance improvements, or amortizing
training time when changing architectures between the teacher and student
network. In the case of reinforcement learning, this technique has also been
applied to distill teacher policies to students. Until now, policy distillation
required access to a simulator or real world trajectories.
  In this paper we introduce a simulator-free approach to knowledge
distillation in the context of reinforcement learning. A key challenge is
having the student learn the multiplicity of cases that correspond to a given
action. While prior work has shown that data-free knowledge distillation is
possible with supervised learning models by generating synthetic examples,
these approaches to are vulnerable to only producing a single prototype example
for each class. We propose an extension to explicitly handle multiple
observations per output class that seeks to find as many exemplars as possible
for a given output class by reinitializing our data generator and making use of
an adversarial loss.
  To the best of our knowledge, this is the first demonstration of
simulator-free knowledge distillation between a teacher and a student policy.
This new approach improves over the state of the art on data-free learning of
student networks on benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10), and we
also demonstrate that it specifically tackles issues with multiple input modes.
We also identify open problems when distilling agents trained in high
dimensional environments such as Pong, Breakout, or Seaquest.
