The invention provides a method for avoiding an obstacle based on a deep double-Q network countermeasure architecture. The method comprises the steps that a monocular vision RGB image is adopted, anda corresponding depth image is obtained; based on a countermeasure network and double-Q network mechanism, a model is trained in a simulator, and knowledge leant from a simulation test can be seamlessly transferred into a new scene in the real word; a machine learns how to avoid the obstacle on the simulator, and the deep information forecasting can be conducted even in an extremely noisy RGB image. According to the method, in combination with the double-current countermeasure network, monocular vision obstacle avoidance is conducted, the end-to-end high-speed learning of the obstacle avoidance task is achieved with the limited computing resources based on the double-Q network by the adoption of the countermeasure network architecture and can be directly transferred into the real robot completely, complex modeling and parameter adjustment of a traditional path planner are avoided, the performance can be improved greatly, and the training speed is increased greatly; and in addition, a variety of robot operating environment information is provided by a monocular camera, the cost is low, the weight is low, and the method is applicable to various platforms.