# Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning
## 1 Introduction  
In recent years, the improvement of Unmanned Aerial Vehicle (UAV) [1, 2, 3, 4] performance and the increase in task complexity have promoted the development of distributed cluster technology [5, 6, 7]. The UAV flight control system [8] is a strong real-time embedded system with high computational complexity. It includes not only the software processing system [9] composed of flight control software and an operating system, but also the hardware circuit system [10] composed of an optical fiber communication chip and a high-performance processor. Therefore, in the process of UAV flight control task testing, the system has strict constraints on the combination logic and sequential logic when software and hardware coordinate the operation. Among them, the panoramic test of the UAV cluster with a complex and changeable working environment and a long working cycle becomes very important. In the UAV cluster test system, there are two important problems that need to be solved urgently: the single UAV testing and the testing task scheduling. First of all, in the test system, a large number of scenarios exist in which there are constraints among the values of the same sensor parameters and different sensor parameters. Directly ignoring these constraints and conditions will make the test incentive data invalid and even reduce the overall test coverage of the system. At the same time, the UAV cluster control system has strict time sequence requirements for multi-task scheduling. The input sequence and parameter values of task data will result in different test processing results at different times. If a single test node is used to process the testing task, the overall performance requirements of the UAV cluster test system cannot be satisfied due to the upper limit of the ability of the test tool at the single end. On the other hand, the scheduling method of testing tasks is an important part of the UAV cluster test system. The ideal scheduling method can greatly improve the processing efficiency and resource utilization of test nodes, and is conducive to improving the overall performance of the system. Testing task scheduling is generally applied to scenarios where the number of test cases is large and the mutual constraint relationship is complex, or the system has high real-time requirements. Therefore, an unreasonable scheduling method may lead to unreasonable test case allocation, imbalanced resource allocation, and lower work efficiency and reliability of test nodes. Therefore, it is necessary to research and propose a reasonable and reliable testing task scheduling method. In order to explore the strategy of large-scale testing task execution and resource scheduling, this work designs and proposes an innovative distributed test method for UAV clusters. Firstly, a testing task scheduling problem in the UAV cluster environment is proposed based on the current UAV cluster testing problem, and then the mathematical model of testing task execution in a distributed network environment is established by using the Deep Deterministic Policy Gradient (DDPG). The deep reinforcement learning network is constructed by means of iterative training, and better testing task deployment strategy decisions are derived. The experimental results show that the method proposed in this work can achieve load balancing of the test system while minimizing bandwidth resource consumption and assisting the system to obtain long-term benefits. The main contributions of this work are as follows:  
1. A distributed testing task execution problem in the UAV cluster environment is proposed;
2. By taking advantage of the DDPG, the mathematical problem model of testing task execution in a distributed network environment is established. Deep reinforcement learning networks are iteratively trained to derive better testing task deployment strategy decisions.