AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)
Yimian Ding, Jingzehua Xu, Yiyuan Yang, Guanwen Xie, Xinqi Wang, Shuai Zhang
- Year
- 2026
- Access
- Open access
Abstract
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.
Keywords
Related papers
The Organization of Behavior
D. O. Hebb
2005
Fractional Brownian Motions, Fractional Noises and Applications
Benoît B. Mandelbrot, John W. Van Ness
1968
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi +7 more
2021
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar +7 more
2018