AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916018125799424 |
|---|---|
| author | Ding, Yimian Xu, Jingzehua Yang, Yiyuan Xie, Guanwen Wang, Xinqi Zhang, Shuai |
| author_facet | Ding, Yimian Xu, Jingzehua Yang, Yiyuan Xie, Guanwen Wang, Xinqi Zhang, Shuai |
| contents | Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process (AoI-MDP) to enhance underwater tasks by modeling observation delay as signal delay and including it in the state space. AoI-MDP also introduces wait time in the action space and integrates AoI with reward functions, optimizing information freshness and decision-making using reinforcement learning. Simulations show AoI-MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks. To accelerate relevant research, we have made the codes available as open-source at https://github.com/Xiboxtg/AoI-MDP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16777 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract) Ding, Yimian Xu, Jingzehua Yang, Yiyuan Xie, Guanwen Wang, Xinqi Zhang, Shuai Systems and Control Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process (AoI-MDP) to enhance underwater tasks by modeling observation delay as signal delay and including it in the state space. AoI-MDP also introduces wait time in the action space and integrates AoI with reward functions, optimizing information freshness and decision-making using reinforcement learning. Simulations show AoI-MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks. To accelerate relevant research, we have made the codes available as open-source at https://github.com/Xiboxtg/AoI-MDP. |
| title | AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract) |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.16777 |