BF++: a language for general-purpose neural program synthesis

Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models, where it is often difficult to incorporate expert knowledge into the models or let experts review and validate the learned decision mechanisms. Knowledge-insertion and model review are important requirements in many applications involving human health and safety. One way to bridge the gap between data- and knowledge-driven systems is program synthesis: replacing a neural network that outputs decisions with one that generates decision-making code in some programming language. We propose a new programming language, BF++, designed specifically for neural program synthesis in a Partially Observable Markov Decision Process (POMDP) setting and generate programs for a number of standard OpenAI Gym benchmarks.