This paper introduces a novel recurrent neural network model for visual attention, called Recurrent Attention Model (RAM), which adaptively selects regions of interest in images or videos to process at high resolution, thereby reducing computational costs. Unlike traditional convolutional networks, RAM's computational complexity is independent of input size, allowing it to handle large images efficiently. The model is trained using reinforcement learning to learn task-specific policies, enabling it to focus attention on relevant parts of the scene. It is applied to both image classification and dynamic visual control tasks, where it outperforms convolutional networks, especially in cluttered environments. The model processes inputs sequentially, using a "glimpse" sensor to extract localized information and an RNN to maintain an internal state that guides attention and decision-making. The model's architecture includes a glimpse network, an internal state network, and action networks that determine where to look and what action to take. Training involves policy gradient methods, with the model learning to maximize cumulative rewards. Experiments on image classification tasks, including MNIST and cluttered MNIST, show that RAM achieves lower error rates than convolutional networks, particularly in cluttered scenarios. In dynamic environments, such as a simple game, RAM learns to track and catch a ball by focusing attention on relevant regions, demonstrating its effectiveness in complex tasks. The model's ability to adaptively select regions and ignore irrelevant information makes it efficient and effective for visual processing tasks.This paper introduces a novel recurrent neural network model for visual attention, called Recurrent Attention Model (RAM), which adaptively selects regions of interest in images or videos to process at high resolution, thereby reducing computational costs. Unlike traditional convolutional networks, RAM's computational complexity is independent of input size, allowing it to handle large images efficiently. The model is trained using reinforcement learning to learn task-specific policies, enabling it to focus attention on relevant parts of the scene. It is applied to both image classification and dynamic visual control tasks, where it outperforms convolutional networks, especially in cluttered environments. The model processes inputs sequentially, using a "glimpse" sensor to extract localized information and an RNN to maintain an internal state that guides attention and decision-making. The model's architecture includes a glimpse network, an internal state network, and action networks that determine where to look and what action to take. Training involves policy gradient methods, with the model learning to maximize cumulative rewards. Experiments on image classification tasks, including MNIST and cluttered MNIST, show that RAM achieves lower error rates than convolutional networks, particularly in cluttered scenarios. In dynamic environments, such as a simple game, RAM learns to track and catch a ball by focusing attention on relevant regions, demonstrating its effectiveness in complex tasks. The model's ability to adaptively select regions and ignore irrelevant information makes it efficient and effective for visual processing tasks.