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. Source code is available at https:/github.com/vadim0x60/cibi
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