How does reinforcement learning differ from deep learning?

Reinforcement learning (RL) and deep learning (DL) are distinct approaches within machine learning, each addressing different types of problems. Reinforcement learning focuses on training agents to make decisions by interacting with an environment to maximize cumulative rewards. For example, an RL agent might learn to play a video game by trial and error, receiving points for successful moves. In contrast, deep learning uses neural networks with multiple layers to automatically learn patterns from large datasets. A DL model, for instance, might classify images by analyzing pixel data. While RL deals with sequential decision-making, DL is primarily about extracting features and making predictions from static data.

The technical frameworks and training processes differ significantly. In RL, an agent explores an environment by taking actions, observing outcomes, and adjusting its strategy based on rewards or penalties. This requires balancing exploration (trying new actions) and exploitation (using known effective actions). For example, a self-driving car simulation might reward the agent for staying on the road. Deep learning, however, relies on labeled datasets and backpropagation to adjust network weights. Training a DL model for speech recognition involves feeding audio data and adjusting layers to minimize prediction errors. While RL often operates in dynamic, feedback-driven scenarios, DL typically processes fixed data batches.

Use cases and applications highlight their differences further. RL excels in scenarios requiring adaptive decision-making over time, such as robotics (e.g., training a robot arm to grasp objects) or game AI (e.g., AlphaGo). These tasks involve long-term planning and handling uncertainty. Deep learning, meanwhile, dominates tasks like natural language processing (e.g., translating text) or computer vision (e.g., detecting tumors in X-rays), where large amounts of data can be processed to identify complex patterns. While RL and DL can overlap—such as using deep neural networks to approximate policies in RL (Deep Q-Networks)—their core objectives and methods remain separate. RL prioritizes sequential optimization, while DL emphasizes hierarchical feature learning.