To solve multi-robot navigation with traditional methods, many algorithms can be used in combination, both for navigation and for the cooperation of these robots. These traditional methods using multiple algorithms are costly. Deep reinforcement learning (DRL) is simpler and less costly when compared to traditional methods. Nowadays, it is tried to solve real-world problems with DRL for these reasons. In this study, it has been tried to solve the multi-robot navigation problem with DRL. In the system in the proposed approach, there is a synchronous environment and more than one robot, target and obstacle in this environment. The robots in the environment move by selecting an action, respectively. At the same time, the robots as a dynamic obstacle for other robots. The robots try to reach their targets in the shortest path without any collision. At the same time, the robots try to plan paths so that they do not collide with another robot or extend the path of another robot. In order to provide these, multi-agent DQN algorithms, target-oriented state data, and reinforced adaptive reward mechanism were used. The system in the proposed approach was evaluated as the navigation success of a single robot, the navigation success of the multi-robot system, and the success rate according to the number of robots per unit square.
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