# Learning a Reward Function for User-Preferred Appliance Scheduling

arXiv: https://arxiv.org/abs/2310.07389 · HTML: https://mlsanity.com/paper/2310.07389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 11 Oct 2023.

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

- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning](https://mlsanity.com/paper/1803.11347/) (2018)
- [Asking Easy Questions: A User-Friendly Approach to Active Reward Learning](https://mlsanity.com/paper/1910.04365/) (2019)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings](https://mlsanity.com/paper/2006.14156/) (2020)
- [MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response](https://mlsanity.com/paper/2304.04086/) (2023)

## Abstract (arXiv)

Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End users highly value their privacy and control, and want to be included in the service design and decision-making process when creating the daily appliance operation schedules. Furthermore, unless they are financially or environmentally motivated, they are generally not prepared to sacrifice their comfort to help balance the power system. In this paper, we present an inverse-reinforcement-learning-based model that helps create the end users' daily appliance schedules without asking them to explicitly state their needs and wishes. By using their past consumption data, the end consumers will implicitly participate in the creation of those decisions and will thus be motivated to continue participating in the provision of demand response services.