Approximate Control for Continuous-Time POMDPs
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917600453197824 |
|---|---|
| author | Eich, Yannick Alt, Bastian Koeppl, Heinz |
| author_facet | Eich, Yannick Alt, Bastian Koeppl, Heinz |
| contents | This work proposes a decision-making framework for partially observable systems in continuous time with discrete state and action spaces. As optimal decision-making becomes intractable for large state spaces we employ approximation methods for the filtering and the control problem that scale well with an increasing number of states. Specifically, we approximate the high-dimensional filtering distribution by projecting it onto a parametric family of distributions, and integrate it into a control heuristic based on the fully observable system to obtain a scalable policy. We demonstrate the effectiveness of our approach on several partially observed systems, including queueing systems and chemical reaction networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01431 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Approximate Control for Continuous-Time POMDPs Eich, Yannick Alt, Bastian Koeppl, Heinz Machine Learning Systems and Control Quantitative Methods This work proposes a decision-making framework for partially observable systems in continuous time with discrete state and action spaces. As optimal decision-making becomes intractable for large state spaces we employ approximation methods for the filtering and the control problem that scale well with an increasing number of states. Specifically, we approximate the high-dimensional filtering distribution by projecting it onto a parametric family of distributions, and integrate it into a control heuristic based on the fully observable system to obtain a scalable policy. We demonstrate the effectiveness of our approach on several partially observed systems, including queueing systems and chemical reaction networks. |
| title | Approximate Control for Continuous-Time POMDPs |
| topic | Machine Learning Systems and Control Quantitative Methods |
| url | https://arxiv.org/abs/2402.01431 |