# GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management

arXiv: https://arxiv.org/abs/2110.06396 · HTML: https://mlsanity.com/paper/2110.06396/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management](https://mlsanity.com/paper/2012.10504/) (2020)
- [Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems](https://mlsanity.com/paper/2003.03681/) (2020)
- [SuperSuit: Simple Microwrappers for Reinforcement Learning Environments](https://mlsanity.com/paper/2008.08932/) (2020)
- [Network-Cognizant Voltage Droop Control for Distribution Grids](https://mlsanity.com/paper/1702.02969/) (2017)
- [Stochastic Hosting Capacity in LV Distribution Networks](https://mlsanity.com/paper/1902.08780/) (2019)

## Built on by (most cited first, 12 of 12)

- [PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2111.05969/) (2021)
- [CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities](https://mlsanity.com/paper/2405.03848/) (2024)
- [Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility](https://mlsanity.com/paper/2305.18875/) (2023)
- [SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning](https://mlsanity.com/paper/2412.08293/) (2024)
- [MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn](https://mlsanity.com/paper/2301.01148/) (2022)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks](https://mlsanity.com/paper/2408.03685/) (2024)
- [Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads](https://mlsanity.com/paper/2301.02593/) (2023)
- [Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems](https://mlsanity.com/paper/2404.15583/) (2024)
- [Ad Hoc Teamwork in the Presence of Adversaries](https://mlsanity.com/paper/2208.05071/) (2022)
- [MuFlex: A Scalable, Physics-based Platform for Multi-Building Flexibility Analysis and Coordination](https://mlsanity.com/paper/2508.13532/) (2025)
- [PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning](https://mlsanity.com/paper/2609.36052/) (2026)

## Abstract (arXiv)

Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network. Intelligent control of smart inverters and other smart building energy management systems can be leveraged to alleviate these issues. GridLearn is a multiagent reinforcement learning platform that incorporates both building energy models and power flow models to achieve grid level goals, by controlling behind-the-meter resources. This study demonstrates how multi-agent reinforcement learning can preserve building owner privacy and comfort while pursuing grid-level objectives. Building upon the CityLearn framework which considers RL for building-level goals, this work expands the framework to a network setting where grid-level goals are additionally considered. As a case study, we consider voltage regulation on the IEEE-33 bus network using controllable building loads, energy storage, and smart inverters. The results show that the RL agents nominally reduce instances of undervoltages and reduce instances of overvoltages by 34%.