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.

Deep reinforcement learning has seen great success across a breadth of tasks such as in game playing and robotic manipulation. However, the modern practice of attempting to learn tabula rasa disregards the logical structure of many domains and the wealth of readily-available human domain experts' knowledge that could help ``warm start'' the learning process. Further, learning from demonstration techniques are not yet sufficient to infer this knowledge through sampling-based mechanisms in large state and action spaces, or require immense amounts of data. We present a new reinforcement learning architecture that can encode expert knowledge, in the form of propositional logic, directly into a neural, tree-like structure of fuzzy propositions that are amenable to gradient descent. We show that our novel architecture is able to outperform reinforcement and imitation learning techniques across an array of canonical challenge problems for artificial intelligence.