Multiagent inspection system via minimax multiagent deep deterministic policy gradient

The inspection system based on multi-agent reinforcement learning offers extensive application potential, especially in achieving efficient and reliable autonomous inspection tasks in complex and unstable environments. This paper proposes an inspection system architecture based on the Minimax Multi-agent Deep Deterministic Policy Gradient (M3DDPG) algorithm, integrating environmental perception and visual detection technologies. First, an environmental sensor module was designed to collect critical parameters such as temperature, humidity, and smoke in real-time, providing comprehensive environmental awareness. Second, a distributed robot architecture based on the Robot Operating System (ROS) was constructed to ensure efficient communication and collaboration among multiple agents. Then, leveraging the robustness advantage of the M3DDPG algorithm compared to MADDPG, a multi-agent reinforcement learning control framework was implemented, enhancing the system's robustness and task execution efficiency in dynamic environments. Finally, YOLO object detection technology was adopted for real-time environmental inspection, returning images and detection results to provide visual support for inspection tasks. Experimental results demonstrate strong adaptability and robustness in complex environments, offering new insights and practical references for applying multi-agent reinforcement learning in the field of inspection.