Convolutional neural networks (CNNs) are used in reinforcement learning to process and extract features from high-dimensional inputs, such as images or video frames. They are particularly useful when the environment is represented as raw pixel data, as CNNs can automatically detect patterns like edges, textures, and shapes without requiring manual feature extraction.
In deep reinforcement learning, CNNs are often used as the function approximators for value functions or policies. For instance, in Deep Q-learning (DQN), a CNN processes the raw pixel input (such as from a video game) and generates a Q-value for each possible action, allowing the agent to select the best action based on the visual input.
CNNs enable reinforcement learning models to perform tasks like playing video games (e.g., Atari games), robotic control, and autonomous driving, where raw sensory data needs to be processed efficiently for decision-making.