# Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction

arXiv: https://arxiv.org/abs/2103.07919 · HTML: https://mlsanity.com/paper/2103.07919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 14 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Memory-based control with recurrent neural networks](https://mlsanity.com/paper/1512.04455/) (2015)
- [The Scenario Approach for Stochastic Model Predictive Control with Bounds on Closed-Loop Constraint Violations](https://mlsanity.com/paper/1307.5640/) (2013)
- [Deep Reinforcement Learning for Optimal Control of Space Heating](https://mlsanity.com/paper/1805.03777/) (2018)
- [Model Predictive HVAC Control with Online Occupancy Model](https://mlsanity.com/paper/1403.4662/) (2014)

## Abstract (arXiv)

The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings. Inspired by recent advances in reinforcement learning (RL), this paper aims at assessing the potential of RL in building climate control problems with occupant interaction. We apply a recent RL approach, called DDPG (deep deterministic policy gradient), for the continuous building control tasks and assess its performance with simulation studies in terms of its ability to handle (a) the partial state observability due to sensor limitations; (b) complex stochastic system with high-dimensional state-spaces, which are jointly continuous and discrete; (c) uncertainties due to ambient weather conditions, occupant's behavior, and comfort feelings. Especially, the partial observability and uncertainty due to the occupant interaction significantly complicate the control problem. Through simulation studies, the policy learned by DDPG demonstrates reasonable performance and computational tractability.