Enhancing Information Freshness: An AoI Optimized Markov Decision Process
For researchers working on underwater AUV operations, this work addresses the bottleneck of observation delay by integrating AoI into MDP, but the approach is incremental as it extends existing MDP frameworks with a delay component.
The paper proposes an AoI-optimized Markov decision process (AoI-MDP) to address observation delays in underwater AUV tasks, achieving joint optimization of information freshness and decision-making. Simulations show AoI-MDP effectively minimizes AoI and improves task performance.
Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly failed due to the observation delay caused by information limitation in the information updating networks. In this study, we present an AoI optimized Markov decision process (AoI-MDP) to improve the performance of underwater tasks. Specifically, AoI-MDP models observation delay as timing delay through statistical delay formulation, and includes this delay as a new component in the state space. Additionally, we introduce wait time in the action space, and integrate AoI with reward functions to achieve joint optimization of information freshness and decision-making for AUVs leveraging RL for training. Finally, we apply this approach to the multi-AUV data collection task scenario as an example. Simulation results highlight the feasibility of AoI-MDP, which effectively minimizes AoI while showcasing superior performance in the task. To accelerate relevant research in this field, we have made the simulation codes available as open-source.