## 2 Preliminary Techniques and System Model
### Deep Reinforcement Learning  
DRL is an advanced branch of machine learning that combines RL with deep neural networks. It enables agents to make a sequence of decisions by interacting with an environment, leveraging the representational power of deep learning to handle complex state and action spaces. Unlike traditional RL, which may struggle with high-dimensional data, DRLcan effectively process and learn from large and intricate datasets, making it particularly suited for dynamic and complex environments like MANETs.  
In the context of MANETs, DRL plays a crucial role in optimizing routing decisions amidst highly dynamic and unpredictable network conditions. By continuously learning from the network's state and the outcomes of its actions, a DRL agent can adaptively adjust routing paths to enhance performance metrics such as throughput, latency, and energy efficiency. This adaptability is essential in MANETs, where nodes frequently move, leading to constantly changing network topologies and varying link qualities. One of the primary advantages of DRL in MANETs is its ability to autonomously discover optimal routing policies without requiring exhaustive prior knowledge of the network topology or traffic patterns. This autonomy enables the network to swiftly adapt to changes, such as node mobility, varying link qualities, and fluctuating traffic demands, thereby maintaining robust and efficient communication links. Additionally, DRL facilitates the development of intelligent routing strategies that can balance multiple conflicting objectives. For instance, a DRL-based routing protocol can simultaneously aim to minimize energy consumption, reduce end-to-end delay, and maximize PDR by learning to prioritize certain routes over others based on real-time network feedback.  
Moreover, DRL enhances the scalability of routing protocols in MANETs. Traditional routing protocols may struggle with increased computational and communication overhead as the network size grows. In contrast, DRL algorithms can efficiently scale by leveraging deep neural networks to generalize learning across diverse network states and actions. This scalability ensures that the routing protocol remains effective even as the network expands in size and complexity. Furthermore, DRL contributes to the resilience of MANETs by enabling proactive and reactive adaptations to network anomalies and failures. For example, in the event of a link failure or node dropout, a DRL agent can quickly learn to reroute traffic through alternative paths, thereby minimizing service disruptions and maintaining overall network stability. This dynamic response capability is vital for sustaining reliable communication in environments where network conditions are perpetually evolving. The integration of DRL into MANETs also promotes energy-efficient routing decisions. By considering factors such as residual energy and link stability, DRL algorithms can optimize routing paths to conserve energy, thereby prolonging the operational lifetime of individual nodes and the network as a whole. This energy-aware approach is particularly important in MANETs, where nodes are often powered by limited energy sources.  
Overall, DRL provides a dynamic and adaptable approach to routing in MANETs. Its capacity for autonomous policy discovery, multi-objective optimization, scalability, and resilience makes it an indispensable component of advanced routing protocols aimed at achieving high performance and reliability in MANET environments.