  Recent advances in deep reinforcement learning require a large amount of
training data and generally result in representations that are often over
specialized to the target task. In this work, we present a methodology to study
the underlying potential causes for this specialization. We use the recently
proposed projection weighted Canonical Correlation Analysis (PWCCA) to measure
the similarity of visual representations learned in the same environment by
performing different tasks.
  We then leverage our proposed methodology to examine the task dependence of
visual representations learned on related but distinct embodied navigation
tasks. Surprisingly, we find that slight differences in task have no measurable
effect on the visual representation for both SqueezeNet and ResNet
architectures. We then empirically demonstrate that visual representations
learned on one task can be effectively transferred to a different task.
