In the past, in order to improve the performance of an algorithm on a certain task, it is often necessary to use prior knowledge to manually design corresponding features to help the algorithm better converge to the optimal solution. This type of feature extraction method is often strongly related to the specific task. Once the scenario changes, these artificially designed features and prior settings cannot adapt to the new scenario, and people often need to redesign the algorithms.
Designing a universal intelligent mechanism that can automatically learn and self-adjust like the human brain has always been the common vision of human beings. Deep learning is one of the algorithms closest to general intelligence. In the computer vision field, previous methods that need to desing features for specific tasks and add a priori assumptions have been abandoned by deep learning algorithms. At present, almost all algorithms in image recognition, object detection, and semantic segmentation are based on end-to-end deep learning models, which present good performance and strong adaptability. On the Atari game platform, the DQN algorithm designed by DeepMind can reach human equivalent level in 49 games under the same algorithm, model structure, and hyperparameter settings, showing a certain degree of general intelligence. Figure 1-14 is the network structure of the DQN algorithm. It is not designed for a certain game but can control 49 games on the Atari game platform
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