  Active network management (ANM) of electricity distribution networks include
many complex stochastic sequential optimization problems. These problems need
to be solved for integrating renewable energies and distributed storage into
future electrical grids. In this work, we introduce Gym-ANM, a framework for
designing reinforcement learning (RL) environments that model ANM tasks in
electricity distribution networks. These environments provide new playgrounds
for RL research in the management of electricity networks that do not require
an extensive knowledge of the underlying dynamics of such systems. Along with
this work, we are releasing an implementation of an introductory
toy-environment, ANM6-Easy, designed to emphasize common challenges in ANM. We
also show that state-of-the-art RL algorithms can already achieve good
performance on ANM6-Easy when compared against a model predictive control (MPC)
approach. Finally, we provide guidelines to create new Gym-ANM environments
differing in terms of (a) the distribution network topology and parameters, (b)
the observation space, (c) the modelling of the stochastic processes present in
the system, and (d) a set of hyperparameters influencing the reward signal.
Gym-ANM can be downloaded at https://github.com/robinhenry/gym-anm.
