Mechanical arm plug-in control method and device based on reinforcement learning, equipment and medium

The invention relates to the technical field of mechanical arm control, and provides a mechanical arm plug-in control method and device based on reinforcement learning, equipment and a medium, the method comprises the steps that middle point information is set according to container pose information and a preset distance, and the middle point information is information of a preset point located over a container model; training a first reinforcement learning model based on the random first initialization pose and the intermediate point information, wherein the first reinforcement learning model is used for generating a first movement strategy; training a second reinforcement learning model based on a random second initialization pose, the trained first reinforcement learning model, the intermediate point information and the container pose information, wherein the second reinforcement learning model is used for generating a second movement strategy; deploying the trained first reinforcement learning model and t