  Policy specification is a process by which a human can initialize a robot's
behaviour and, in turn, warm-start policy optimization via Reinforcement
Learning (RL). While policy specification/design is inherently a collaborative
process, modern methods based on Learning from Demonstration or Deep RL lack
the model interpretability and accessibility to be classified as such. Current
state-of-the-art methods for policy specification rely on black-box models,
which are an insufficient means of collaboration for non-expert users: These
models provide no means of inspecting policies learnt by the agent and are not
focused on creating a usable modality for teaching robot behaviour. In this
paper, we propose a novel machine learning framework that enables humans to 1)
specify, through natural language, interpretable policies in the form of
easy-to-understand decision trees, 2) leverage these policies to warm-start
reinforcement learning and 3) outperform baselines that lack our natural
language initialization mechanism. We train our approach by collecting a
first-of-its-kind corpus mapping free-form natural language policy descriptions
to decision tree-based policies. We show that our novel framework translates
natural language to decision trees with a 96% and 97% accuracy on a held-out
corpus across two domains, respectively. Finally, we validate that policies
initialized with natural language commands are able to significantly outperform
relevant baselines (p < 0.001) that do not benefit from our natural
language-based warm-start technique.
