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Neurogenetic Programming Framework for Explainable Reinforcement Learning

2021-02-08 14:26:02
Vadim Liventsev, Aki Härmä, Milan Petković
arXiv_AI
           
Abstract

Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models. We propose a novel method that combines both approaches using a concept of a virtual neuro-genetic programmer: using evolutionary methods as an alternative to gradient descent for neural network training}, or scrum team. We demonstrate its ability to provide performant and explainable solutions for various OpenAI Gym tasks, as well as inject expert knowledge into the otherwise data-driven search for solutions.

Abstract (translated)

URL

https://arxiv.org/abs/2102.04231

PDF

https://arxiv.org/pdf/2102.04231.pdf

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