  Gym-ANM is a Python package that facilitates the design of reinforcement
learning (RL) environments that model active network management (ANM) tasks in
electricity networks. Here, we describe how to implement new environments and
how to write code to interact with pre-existing ones. We also provide an
overview of ANM6-Easy, an environment designed to highlight common ANM
challenges. Finally, we discuss the potential impact of Gym-ANM on the
scientific community, both in terms of research and education. We hope this
package will facilitate collaboration between the power system and RL
communities in the search for algorithms to control future energy systems.
