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This report summarizes work performed as part of an internship at INRIA, inpartial requirement for the completion of a master degree in math andinformatics. The goal of the internship was to develop a software environmentto simulate electricity transmission in a power grid and actions performed byoperators to maintain this grid in security. Our environment lends itself toautomate the control of the power grid with reinforcement learning agents,assisting human operators. It is amenable to organizing benchmarks, including achallenge in machine learning planned by INRIA and RTE for 2019. Our framework,built on top of open-source libraries, is available athttps://github.com/MarvinLer/pypownet. In this report we present intermediaryresults and its usage in the context of a reinforcement learning game.

Design and implementation of an environment for Learning to Run a Power Network (L2RPN)