# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach  
[PERSON]  
1 Beijing Key Laboratory of Transportation Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China 1 [PERSON]
has become the main research direction with respect to improving monitoring, positioning, and perception capabilities in marine environments [7]. Nowadays, for the problem of cooperative coverage path planning of UAV swarms, most studies adopt intelligent optimization algorithms such as particle swarm optimization and the genetic algorithm for stochastic search. However, the marine environment is complex and ever-changing, and the above algorithms can only obtain optimal and suboptimal solutions through multiple iterations, which cannot be generalized to dynamic, unknown environments. Furthermore, the formulated problem is a typical non-convex problem, which are extremely difficult to solve, and its complexity increases sharply with the number of parameters to be optimized [8; 9]. Therefore, how to plan the path of UAVs reasonably in marine monitoring tasks to achieve efficient coverage of the target area is still subject to a series of challenges in academia. Fortunately, with the rapid development of artificial intelligence, the combination of deep reinforcement learning (DRL) algorithms and UAVs enables more intelligent and autonomous execution of tasks [10]. Traditional reinforcement learning algorithms only consider the interaction between a single agent and the environment, making it difficult to effectively handle the problem of multiagent cooperative decision making [11]. [PERSON] et al. [12] conducted a comprehensive survey of multiagent DRL. In multiagent reinforcement learning, multiple agents learn and influence each other to optimize their cooperative decision making. In the context of ocean monitoring applications, multiple UAVs need to work together to achieve efficient target coverage, and using multiagent reinforcement learning can better address this issue. Therefore, compared to traditional reinforcement learning algorithms, the use of multiagent reinforcement learning has a broader and more significant application prospect in multi-UAV path-planning research based on ocean monitoring. Compared with other multiagent algorithms, the MATD3 algorithm has several advantages. (i) The MATD3 algorithm ensures stability and improves the robustness of the algorithm in multiagent decision making. (ii) The algorithm also uses double Q networks, which can more accurately evaluate the value of actions. MATD3 not only coordinates the actions of multiple UAVs but also handles uncertainty caused by environmental and UAV behavioral changes. Therefore, using the MATD3 algorithm to solve multi-UAV path-planning problems in ocean monitoring is a promising research direction. In this paper, considering the energy limitation and obstacle-avoidance ability of UAVs, we investigate how to achieve maximum coverage of the target area in ocean monitoring scenarios. Specifically, we propose a path-planning method based on a multiagent DRL framework to achieve maximum coverage of the target area. The traditional MATD3 algorithm can only consider the current state of the agents, ignoring the historical interaction information among the agents and environmental changes, which leads to poor path  
Figure 1: Multi-UAVs perform marine data-monitoring missions in target areas. planning results. Our method uses long short-term memory (LSTM) to store and utilize previous states, which helps to solve the path-planning problem in cases of incomplete information. By leveraging multiagent collaboration and experience sharing in MATD3, we address the non-convex problem of optimizing the objective function. Experimental results demonstrate that our method achieves good performance in terms of coverage paths in ocean monitoring scenarios. The main contributions of this work can be summarized as follows:  
1.