Exploiting Language Instructions for Interpretable and Compositional
  Reinforcement Learning
  In this work, we present an alternative approach to making an agent
compositional through the use of a diagnostic classifier. Because of the need
for explainable agents in automated decision processes, we attempt to interpret
the latent space from an RL agent to identify its current objective in a
complex language instruction. Results show that the classification process
causes changes in the hidden states which makes them more easily interpretable,
but also causes a shift in zero-shot performance to novel instructions. Lastly,
we limit the supervisory signal on the classification, and observe a similar
but less notable effect.
