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We present the PowerGridworld software package to provide users with alightweight, modular, and customizable framework for creatingpower-systems-focused, multi-agent Gym environments that readily integrate withexisting training frameworks for reinforcement learning (RL). Although manyframeworks exist for training multi-agent RL (MARL) policies, none can rapidlyprototype and develop the environments themselves, especially in the context ofheterogeneous (composite, multi-device) power systems where power flowsolutions are required to define grid-level variables and costs. PowerGridworldis an open-source software package that helps to fill this gap. To highlightPowerGridworld's key features, we present two case studies and demonstratelearning MARL policies using both OpenAI's multi-agent deep deterministicpolicy gradient (MADDPG) and RLLib's proximal policy optimization (PPO)algorithms. In both cases, at least some subset of agents incorporates elementsof the power flow solution at each time step as part of their reward (negativecost) structures.

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems