## 2 Related Work
Improves high-resolution operations due to its careful editing. It is useful in low operations, changing spectrum-sharing situations, but it is slow due to its complexity. MDRL-RA [46] was proposed to improve QoS, which includes low latency and high throughput. It manages all things in the centralized training and decentralized execution of a multi-agent proximal policy optimization technique. LSTM layers are utilized to detect errors. It further improves the transmission success, capacity, and payload delivery, but due to its complex structure, it may cause delay and overhearing. MRL-IPP [47] is based on Q-RTS with a multi-agent procedure that has been used for robotic applications. With the increase in agents, the convergence time decreases, allowing for limited training iterations. It is more scalable and reliable, but more specific, and it needs to be generalized to other applications. All of the above schemes have worked in cognitive networks with RL, but most of them have been applied as a trade-off between different parameters. They implement specific domains and most of the time implement a passive approach, while we need an active approach that works under real-time traffic conditions with other environmental and internal parameters. Table 1 provides an overview of various systems, including their fundamental ideas, advantages, and limitations. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Scheme** & **Parameters Used** & **Advantages** & **Drawback (s)** \\ \hline CAS-IIoT-RL [22] & Adaptive, dynamic decision controls & Enhanced decision making in industrial settings & Limited to simulations, lacks real-time validation \\ RL-IoT [29] & Routing, CR-IoT, CRN, retransmission & Decreased delay and collisions, improved throughput & Specific to routing, generalization may be limited \\ SCA-RL [30] & Bayesian algorithm, idle channel prediction & Reduces spectrum handover, avoids retransmission collisions & Limited adaptability in dynamic environments \\ NOMA [31] & Dynamic Q-learning, spectrum access & Increased throughput and spectrum utilization & Disrupts continuity of packet flow \\ LSTM-RL [27] & Spatiotemporal patterns, DQN, MDP & Optimizes energy, maintains prediction accuracy & High computational cost for long-term prediction \\ MARL [32] & Deep recurrent Q-network, cooperative approach & Validated improvement in cognitive radio networks & Requires high-level coordination \\ RL-IoT [33] & EED minimization, AODV-IoT, ML techniques & Avoids data collision, improved routing & Dependent on specific validation scenarios \\ MA-DCSS [34] & Dec-POMDP, CTDE, conditional probabilities & High detection accuracy, optimal control & Fails in distributed systems \\ MICRC [35] & Multi-objective clustering, RL routing & Better energy enhancement, new network design & High data processing and communication overhead \\ CIRM [36] & ANN, continual learning, brain-based model & Adaptive robotic movement in manufacturing & Informal structure may lack robustness \\ MRL-CSS [28] & Multi-agent DDPG, adaptive CSS & Improved sensing accuracy, cooperative sensing & Not scalable due to high communication cost \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the literature of RL-based schemes and cognitive IoT networks.