## 2 Related Work
Informally, it processes information in parallel with a counter; for unforeseen situations, the movement of the robot is adjusted. MRL-CSS [28] has been proposed to enhance the spectrum sharing in CRNs, using the cooperative spectrum sensing (CSS) method of multi-agent deep reinforcement learning. It is based on Adaptive Partner CSS and multi-agent deep deterministic policy gradient. Its main aim is to reduce sensing accuracy and lower the communication overhead. This scheme has obtained better results in sensing accuracy, but due to greater communication costs, it is not recommended for large-scale networks. CMRL-DG [37] is based on MARL and is used in a learning-based strategy for EH-WSNs. It achieves high network performance by efficiently communicating sensor data. Its agent continuously learns and effectively utilizes the resources, even when sensor failures are encountered by other nodes. The scheme works better in EH-WSN, but it is more specific in its structure, and its performance may decline in delay-tolerant networks. SDTVA [38] is a smart city governance integration system for analyzing big data analytics and cloud computing. It uses sustainable development data from 2018 to 2024 using the Shiny app and PRISMA. It further uses some statistical and analytical results to prove the scheme's legitimacy and working procedures on big data. Specifically, it focuses on evidence mapping, machine learning tools, and bibliometric visualization in data analysis and processing in various procedures. CPSL-CM [39] is based on a safe decision-making model that combines blockchain technology with RL. It focuses mainly on communication security, resource constraints, and routing disturbances. It gets rid of ambiguities and malevolent threats in IoT with flexibility and better efficiency. It works better in fault detection. LSTM-DQN [40] is a short-term memory deep Q-network RL-based technique used to improve energy efficiency in IoT-based target tracking systems. Dynamically selects the most energy-efficient sensor based on a minimum distance state function. This scheme is useful in maintaining efficient target tracking with lower energy consumption. It is better in energy utilization, but requires more calculation in the RL strategy. MOSA-RL [41] is the combination of multihead self-attention and multiple agents. Channel selection and energy efficiency benefit from centralized training and a decentralized execution architecture. The rewards are continuously updated with a dynamic and multi-constraint proportional function. It also helps in distributing attention using a multihead self-attention mechanism. It improves throughput, convergence, and flexibility, but experience more delay and latency in communication. RL-ORI [42] is mainly used to improve individualized healthcareservices. Due to the use of extensive sensors with RL strategies, it dynamically modifies the activities based on individual reactions from real-time data to update its position adaptively. It improves decision-making in the medical processes and increases efficiency. IDR-FRL [43] has ensured high-speed data routing with frequent node relocations, scalability, and energy efficiency. With Federated RL, it manages the load balance and localizes with the routing cost. It has obtained good results in terms of lowering latency and packet loss but has failed in the large-scale scalability problem. MURPPO [44] is a multi-UAV reconnaissance proximal policy scheme to identify and locate radiation sources in urban areas. It is based on distributed RL and on a dual-branch actor structure for controlling and finalizing decision-making. The reward function and the agent combined use a task-specific approach. It ensures better results in localizations and completely ignores the authentication error. DRL-CRS [45] is RL-based for the efficient use of pulse-agile radar systems in crowded areas of spectrum use. The agent updates the waveform with different parameters such as network distortion, bandwidth consumption, and collision avoidance.