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

• URL: http://arxiv.org/abs/2101.09571v3
• Date: Thu, 18 Feb 2021 20:24:02 GMT
• Title: BF++: a language for general-purpose program synthesis
• Authors: Vadim Liventsev, Aki H\"arm\"a and Milan Petkovi\'c
• Abstract summary: Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models. We propose a new programming language, BF++, designed specifically for automatic programming of agents in a Partially Observable Markov Decision Process setting.
• Score: 0.483420384410068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 a symbolic program generated by a neural network or by means of genetic programming. We propose a new programming language, BF++, designed specifically for automatic programming of agents in a Partially Observable Markov Decision Process (POMDP) setting and apply neural program synthesis to solve standard OpenAI Gym benchmarks.

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