论文标题
通过两国Q学习的增强学习图像分类
Image Classification by Reinforcement Learning with Two-State Q-Learning
论文作者
论文摘要
在本文中，提出了一个简单有效的混合分类器，该分类器基于深度学习和强化学习。在这里，Q学习已与两个状态和“两个或三个”动作一起使用。文献中发现的其他技术使用特征图从卷积神经网络中提取，并将其与过去的历史一起使用。这导致了这些方法的技术困难，因为由于特征图的尺寸很大，状态的数量很高。由于所提出的技术仅使用两种Q-验证，因此很简单，因此具有较少数量的优化参数，因此也具有简单的奖励函数。同样，与文献中发现的其他技术相比，该提出的技术使用新颖的动作来处理图像。将所提出的技术的性能与其他最近的算法（如Resnet50，InceptionV3等）进行了比较。所提出的方法在所有使用的数据集上优于其他技术。
In this paper, a simple and efficient Hybrid Classifier is presented which is based on deep learning and reinforcement learning. Here, Q-Learning has been used with two states and 'two or three' actions. Other techniques found in the literature use feature map extracted from Convolutional Neural Networks and use these in the Q-states along with past history. This leads to technical difficulties in these approaches because the number of states is high due to large dimensions of the feature map. Because the proposed technique uses only two Q-states it is straightforward and consequently has much lesser number of optimization parameters, and thus also has a simple reward function. Also, the proposed technique uses novel actions for processing images as compared to other techniques found in literature. The performance of the proposed technique is compared with other recent algorithms like ResNet50, InceptionV3, etc. on popular databases including ImageNet, Cats and Dogs Dataset, and Caltech-101 Dataset. The proposed approach outperforms others techniques on all the datasets used.
