Generative adversarial simulator

Abstract

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
distillingagents trained in high dimensional environments such as Pong, Breakout, or
Seaquest.