  In this work we present a technique to use natural language to help
reinforcement learning generalize to unseen environments. This technique uses
neural machine translation, specifically the use of encoder-decoder networks,
to learn associations between natural language behavior descriptions and
state-action information. We then use this learned model to guide agent
exploration using a modified version of policy shaping to make it more
effective at learning in unseen environments. We evaluate this technique using
the popular arcade game, Frogger, under ideal and non-ideal conditions. This
evaluation shows that our modified policy shaping algorithm improves over a
Q-learning agent as well as a baseline version of policy shaping.
