# Deep Reinforcement Learning for Optimal Control of Space Heating

arXiv: https://arxiv.org/abs/1805.03777 · HTML: https://mlsanity.com/paper/1805.03777/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 7 of 7)

- [Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control](https://mlsanity.com/paper/2203.05434/) (2022)
- [Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks](https://mlsanity.com/paper/2312.03365/) (2023)
- [Deep Reinforcement Learning for Heat Pump Control](https://mlsanity.com/paper/2212.12716/) (2022)
- [Constrained Reinforcement Learning for Safe Heat Pump Control](https://mlsanity.com/paper/2409.19716/) (2024)
- [Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction](https://mlsanity.com/paper/2103.07919/) (2021)
- [Employing Federated Learning for Training Autonomous HVAC Systems](https://mlsanity.com/paper/2405.00389/) (2024)
- [Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning](https://mlsanity.com/paper/2505.01332/) (2025)

## Abstract (arXiv)

Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.