Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2404.03227 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912830241898496 |
|---|---|
| author | Chen, Xingran NaderiAlizadeh, Navid Ribeiro, Alejandro Bidokhti, Shirin Saeedi |
| author_facet | Chen, Xingran NaderiAlizadeh, Navid Ribeiro, Alejandro Bidokhti, Shirin Saeedi |
| contents | We study real-time sampling and estimation of autoregressive Markovian sources in decentralized and dynamic multi-hop networks that share similar structures. Nodes cache neighboring samples and communicate over wireless collision channels. The objective is to minimize the time-average estimation error and/or the age of information under decentralized policies, which we address by developing a unified graphical multi-agent reinforcement learning framework. A key feature of the framework is its transferability, enabled by the fact that the number of trainable parameters is independent of the number of agents, allowing a learned policy to be directly deployed on dynamic yet structurally similar graphs without re-training. Building on this design, we establish rigorous theoretical guarantees on the transferability of the resulting policies. Numerical experiments demonstrate that (i) our method outperforms state-of-the-art baselines on dynamic graphs; (ii) the trained policies transfer well to larger networks, with performance gains increasing with the number of nodes; and (iii) incorporating recurrence is crucial, enhancing resilience to non-stationarity in both independent learning and centralized training with decentralized execution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks Chen, Xingran NaderiAlizadeh, Navid Ribeiro, Alejandro Bidokhti, Shirin Saeedi Signal Processing Machine Learning We study real-time sampling and estimation of autoregressive Markovian sources in decentralized and dynamic multi-hop networks that share similar structures. Nodes cache neighboring samples and communicate over wireless collision channels. The objective is to minimize the time-average estimation error and/or the age of information under decentralized policies, which we address by developing a unified graphical multi-agent reinforcement learning framework. A key feature of the framework is its transferability, enabled by the fact that the number of trainable parameters is independent of the number of agents, allowing a learned policy to be directly deployed on dynamic yet structurally similar graphs without re-training. Building on this design, we establish rigorous theoretical guarantees on the transferability of the resulting policies. Numerical experiments demonstrate that (i) our method outperforms state-of-the-art baselines on dynamic graphs; (ii) the trained policies transfer well to larger networks, with performance gains increasing with the number of nodes; and (iii) incorporating recurrence is crucial, enhancing resilience to non-stationarity in both independent learning and centralized training with decentralized execution. |
| title | Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2404.03227 |