# Meta-Reinforcement Learning for Building Energy Management System

arXiv: https://arxiv.org/abs/2210.12590 · HTML: https://mlsanity.com/paper/2210.12590/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 23 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices](https://mlsanity.com/paper/2008.02790/) (2020)
- [Optimal Demand Response Using Device Based Reinforcement Learning](https://mlsanity.com/paper/1401.1549/) (2014)

## Built on by (most cited first, 4 of 4)

- [Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios](https://mlsanity.com/paper/2303.16685/) (2023)
- [DRDT3: Diffusion-Refined Decision Test-Time Training Model](https://mlsanity.com/paper/2501.06718/) (2025)
- [Communication Load Balancing via Efficient Inverse Reinforcement Learning](https://mlsanity.com/paper/2303.16686/) (2023)
- [STEMS: Spatial-Temporal Enhanced Safe Multi-Agent Coordination for Building Energy Management](https://mlsanity.com/paper/2510.14112/) (2025)

## Abstract (arXiv)

The building sector is one of the largest contributors to global energy consumption. Improving its energy efficiency is essential for reducing operational costs and greenhouse gas emissions. Energy management systems (EMS) play a key role in monitoring and controlling building appliances efficiently and reliably. With the increasing integration of renewable energy, intelligent EMS solutions have received growing attention. Reinforcement learning (RL) has recently been explored for this purpose and shows strong potential. However, most RL-based EMS methods require a large number of training steps to learn effective control policies, especially when adapting to unseen buildings, which limits their practical deployment. This paper introduces MetaEMS, a meta-reinforcement learning framework for EMS. MetaEMS improves learning efficiency by transferring knowledge from previously solved tasks to new ones through group-level and building-level adaptation, enabling fast adaptation and effective control across diverse building environments. Experimental results demonstrate that MetaEMS adapts more rapidly to unseen buildings and consistently outperforms baseline methods across various scenarios.