# Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems

arXiv: https://arxiv.org/abs/2404.15583 · HTML: https://mlsanity.com/paper/2404.15583/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks](https://mlsanity.com/paper/2007.02991/) (2020)
- [Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control](https://mlsanity.com/paper/2006.12841/) (2020)
- [GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management](https://mlsanity.com/paper/2110.06396/) (2021)
- [Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility](https://mlsanity.com/paper/2203.03417/) (2022)
- [Coordination of resources at the edge of the electricity grid: systematic review and taxonomy](https://mlsanity.com/paper/2202.03786/) (2022)
- [Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids](https://mlsanity.com/paper/2005.01979/) (2020)
- [Efficient Methods for Approximating the Shapley Value for Asset Sharing in Energy Communities](https://mlsanity.com/paper/2301.00174/) (2022)
- [What may future electricity markets look like?](https://mlsanity.com/paper/2302.02833/) (2023)

## Built on by (most cited first, 2 of 2)

- [Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16062/) (2026)
- [MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16063/) (2026)

## Abstract (arXiv)

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL.