Convolutional neural networks (CNNs) are primarily used in reinforcement learning (RL) to process high-dimensional visual data, enabling agents to interpret complex environments like images or video frames. Unlike traditional RL methods that rely on handcrafted state representations, CNNs automatically extract spatial features from raw pixel inputs. This is critical in tasks where the environment’s state is visual, such as video games or robotics, where the agent must “see” its surroundings to make decisions. For example, in Atari game-playing agents, CNNs analyze screen pixels to detect game objects, obstacles, and patterns, which the RL algorithm then uses to learn optimal actions.
CNNs excel at reducing the complexity of raw visual data by identifying hierarchical spatial patterns. In RL, this allows agents to focus on relevant features without manual preprocessing. For instance, a self-driving car simulation might use a CNN to process camera feeds, identifying lanes, pedestrians, and traffic signals. The RL agent then maps these features to actions like steering or braking. CNNs also handle partial observability by maintaining spatial relationships across input frames, which is vital in dynamic environments. In DeepMind’s DQN (Deep Q-Network), a CNN processes game frames to estimate Q-values—the expected rewards for actions—enabling the agent to learn policies directly from pixels. This approach avoids the need for manual feature engineering, making it scalable across diverse environments.
Beyond raw pixel processing, CNNs are used in RL for transfer learning and multi-task settings. For example, a CNN pretrained on one RL task (e.g., navigating a maze) can be fine-tuned for a related task (e.g., avoiding dynamic obstacles), accelerating training. CNNs also enable agents to handle varying input resolutions, such as resized images from different camera angles in robotics. However, training CNNs in RL requires careful balancing: the network must learn visual features while the RL algorithm optimizes the policy. Techniques like experience replay (storing past transitions) and frame stacking (using multiple frames as input) help stabilize training. Overall, CNNs bridge the gap between raw sensory data and decision-making, making them indispensable in visually driven RL applications.