  Recent work has shown that augmenting environments with language descriptions
improves policy learning. However, for environments with complex language
abstractions, learning how to ground language to observations is difficult due
to sparse, delayed rewards. We propose Language Dynamics Distillation (LDD),
which pretrains a model to predict environment dynamics given demonstrations
with language descriptions, and then fine-tunes these language-aware pretrained
representations via reinforcement learning (RL). In this way, the model is
trained to both maximize expected reward and retain knowledge about how
language relates to environment dynamics. On SILG, a benchmark of five tasks
with language descriptions that evaluate distinct generalization challenges on
unseen environments (NetHack, ALFWorld, RTFM, Messenger, and Touchdown), LDD
outperforms tabula-rasa RL, VAE pretraining, and methods that learn from
unlabeled demonstrations in inverse RL and reward shaping with pretrained
experts. In our analyses, we show that language descriptions in demonstrations
improve sample-efficiency and generalization across environments, and that
dynamics modelling with expert demonstrations is more effective than with
non-experts.
