Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916092079767552 |
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| author | Lu, Junlin Mannion, Patrick Mason, Karl |
| author_facet | Lu, Junlin Mannion, Patrick Mason, Karl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management Lu, Junlin Mannion, Patrick Mason, Karl Artificial Intelligence 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. |
| title | Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2401.07722 |