## 2 Related Work
### Trust Update Mechanism  
The trust update process involves fine-tuning and revising the trust evaluations of nodes to align with the evolving network environment and node behavior. In [16], [PERSON] et al. put forth an efficient distributed trust model tailored for wireless sensor networks. This model employs a sliding time window concept to facilitate trust value updates. This time window is composed of multiple time slots, each representing a cycle time. Additionally, aging factors \(\beta\): \(\beta=e^{i_{l}-i_{l+1}}\) are introduced with weight values \(w_{i}=\beta\), \(w_{i+1}=1-\beta\) to account for the decay in trust values. Trust update methodologies founded on the sliding time window mechanism find widespread application in the literature [4; 10; 12]. These approaches hinge on both real-time and historical windows to execute the update process.  
From a sociological point of view, trust is a generalization and summary of historical experience, and from a statistical point of view, trust has the property of decaying over time. In [17], [PERSON] et al. introduced a dynamic trust renewal model characterized by multiple constraints. This model incorporates a time aging factor to signify the gradual decay of trust over time. Additionally, it employs a reward and punishment factor to distinguish between the consequences of successful interactions and failed interactions. Several factors are introduced to ensure that trust develops gradually while declining swiftly, mirroring the trust-building process observed in human societies.  
In [18], [PERSON] et al. introduced a trust evaluation update mechanism known as TEUC, which relies on the C4.5 decision tree, that pointed out the problems in the sliding time window mechanism and time forgetting factor for trust updating, designed two trust updating mechanisms based on event triggering and time triggering and defined a reward and punishment factor, activated when a specific quantity of trust evidence shifts from its predefined fuzzy trust level, prompting a trust update. Furthermore, if the time window exceeds its limit, trust updating is triggered, irrespective of whether there is a significant change in node trust evidence. Nonetheless, it is important to clarify that TEUC does not consolidate trust evidence, and it is limited to executing updates for individual trust evidence instances, not accommodating multiple sources simultaneously.  
In [19], [PERSON] et al. introduced the Trust Update Mechanism based on Reinforcement Learning (TUMRL), designed specifically for UASNs. They employ the concept of criticality to assess node importance, enabling customized trust update strategies. By integrating reinforcement learning into the environmental model along with criticality, trust updates are made more efficient and adaptive to changing attack patterns. However, when using reinforcement learning, the weights of the states in the trust update process are limited, and the weights cannot be adjusted adaptively.  
Building upon the diverse trust updating schemes discussed earlier, this paper suggests the development of a deep reinforcement learning-based trust updating model capable of dynamically selecting appropriate trust update strategies.