HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909631992823808 |
|---|---|
| author | He, Jiadong Yu, Liang Chen, Zhiqiang Qiu, Dawei Yue, Dong Strbac, Goran Zhang, Meng Ye, Yujian Wang, Yi |
| author_facet | He, Jiadong Yu, Liang Chen, Zhiqiang Qiu, Dawei Yue, Dong Strbac, Goran Zhang, Meng Ye, Yujian Wang, Yi |
| contents | This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In the proposed method, historical optimal decisions are first generated using a neural network-assisted Hierarchical Model Predictive Control (HMPC) framework. These decisions are then used as expert demonstrations in the AIRL module, which aims to train a discriminator to distinguish expert demonstrations from transitions generated by a reinforcement learning agent policy, while simultaneously updating the agent policy that can produce transitions to confuse the discriminator. The proposed HMPC-AIRL method eliminates the need for explicit thermal dynamics models, prior or predictive knowledge of uncertain parameters, or manually designed reward functions. Simulation results based on real-world traces demonstrate the effectiveness and data efficiency of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management He, Jiadong Yu, Liang Chen, Zhiqiang Qiu, Dawei Yue, Dong Strbac, Goran Zhang, Meng Ye, Yujian Wang, Yi Systems and Control This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In the proposed method, historical optimal decisions are first generated using a neural network-assisted Hierarchical Model Predictive Control (HMPC) framework. These decisions are then used as expert demonstrations in the AIRL module, which aims to train a discriminator to distinguish expert demonstrations from transitions generated by a reinforcement learning agent policy, while simultaneously updating the agent policy that can produce transitions to confuse the discriminator. The proposed HMPC-AIRL method eliminates the need for explicit thermal dynamics models, prior or predictive knowledge of uncertain parameters, or manually designed reward functions. Simulation results based on real-world traces demonstrate the effectiveness and data efficiency of the proposed method. |
| title | HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.00898 |