## 2 Background and Related Works  
Opportunistic Routing has been proposed in the context of multiple applications and scenarios including mobile ad-hoc networks (Figure 1) [11; 12; 13], wireless mesh and sensor networks [14; 15; 16], Delay Tolerant Network (DTN) [17], and for smart grids applications [18]. While traditional MANETs protocols fail with high mobility of the nodes and their heterogeneity, OR exploits the broadcast nature of the wireless medium and pre-selects a set of candidate nodes and delegates the packet forwarding, hop-by-hop and on the fly, to these relays [3]. Thus, the packet can traverse from the source node to the destination node through a set of potential routes. This dynamic selection of route improves the transmission reliability and throughput of the network. While the MANETs algorithm is concerned with the end-to-end path between the source and the destination and deals with frequent link failures due to connections and disconnections by applying route management and recovery strategies OR constructs the route online by pre-selecting a set of neighbor'srelays on a routing protocol metric within the radio frequency transmission range of the source node. These neighbor nodes are called relay nodes or candidate sets. Extensive research and surveys have been conducted on OR aspects. The OR reduces the number of transmissions to a destination by taking advantage of the broadcast nature of the wireless medium. Success on reducing transmission number to a packet between source and destination can improve network performance by reducing end-to-end delay, and the energy consumption of network nodes [19]. The broadcast nature of the wireless medium replicates a packet to multiple nodes per transmission, and the choice of relay nodes affects the efficiency of OR. The source nodes utilize the available network information to calculate the OR metric, which is ordering and assigning a probability to the potential candidate set. The node with a higher probability becomes the new custodian node. One can classify the network information to be utilized to compute the OR metrics as local or global. Local network information mainly depends on the information of neighbors, such as the geographic location of nodes or the link delivery probability. On the other hand, the former one mainly relies on the end-to-end information of the network topology by taking into consideration the number of hops from each neighbor to the given destination [19; 20]. The OR algorithms that consider the network topology information to select and prioritize the candidate set outperform OR algorithms that depend only on the local information of the neighbors. However, the computation cost of the global network information could impact the network performance, especially, and network scalability because of the significant overhead. [PERSON] et al. [21; 22] proposed an opportunistic routing approach called (MLProph), which utilizes machine learning algorithms, namely decision tree and neural networks. MLProph is built on top of the opportunistic routing in PROPHET. The routing approach of MLProph is trained by adopting PROPHET routing data performance metrics, such as node power, speed, and location. Based on the evaluation and analysis of the PROPHET routing data, the machine learning algorithm calculates the transition probability to a neighbor node to check whether the node will be able to deliver the message to the corresponding destination eventually. Accordingly, the likelihood of successful deliveries of data packets is predicted. [PERSON] et al. [23] introduced \(Q^{2}-Routing\), which is a hybrid routing protocol that integrates on-demand route discovery technique with proactive updates of the available routes. Their routing scheme depends on Multi-Agent Reinforcement Learning (MARL). The optimal candidate selection of the next-hop forwarder process learns by trial-and-error, without the need for any prior knowledge about the topology of the network environment.