# Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management

arXiv: https://arxiv.org/abs/2401.07722 · HTML: https://mlsanity.com/paper/2401.07722/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 15 Jan 2024.

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

- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Dynamic Weights in Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1809.07803/) (2018)
- [Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making](https://mlsanity.com/paper/1802.07606/) (2018)
- [Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback](https://mlsanity.com/paper/2302.03805/) (2023)
- [Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach](https://mlsanity.com/paper/2304.14115/) (2023)
- [Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making](https://mlsanity.com/paper/2304.14126/) (2023)

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

- [A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments](https://mlsanity.com/paper/2407.11489/) (2024)
- [Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2409.20258/) (2024)

## Abstract (arXiv)

It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a promising approach to mitigate this problem. Understanding the behaviours and values of energy customers is an example of a scenario where preference inference can be used to gain insights into the values of energy customers with multiple objectives, e.g. cost and comfort. In this work, we applied the state-of-art DemoPI method, i.e., the dynamic weight-based preference inference (DWPI) algorithm in a multi-objective residential energy consumption setting to infer preferences from energy consumption demonstrations by simulated users following a rule-based approach. According to our experimental results, the DWPI model achieves accurate demonstration-based preference inferring in three scenarios. These advancements enhance the usability and effectiveness of multi-objective reinforcement learning (MORL) in energy management, enabling more intuitive and user-friendly preference specifications, and opening the door for DWPI to be applied in real-world settings.