Deep Reinforcement Learning (Deep RL) is a branch of machine learning that combines reinforcement learning (RL) with deep learning techniques. In traditional RL, an agent learns to make decisions by interacting with an environment, receiving rewards or penalties based on its actions, and then adjusting its behavior to maximize long-term rewards. Deep RL enhances this process by using deep neural networks to help the agent understand complex environments and handle large amounts of data.

At its core, the agent in Deep RL observes the environment, takes an action, receives feedback (a reward or punishment), and then updates its strategy. What makes Deep RL powerful is that instead of manually defining features for the agent to learn from, it uses deep neural networks to automatically extract patterns from high-dimensional data. This makes it possible for the agent to handle complex tasks like playing video games, robotic control, and autonomous driving.

For example, in a video game setting, the Deep RL agent might analyze raw pixels from the game screen, learn which in-game actions (like jumping or moving) lead to positive outcomes, and improve its strategy over time. The deep learning component allows the system to handle very intricate tasks that were previously beyond the reach of simple RL algorithms.

This combination of deep learning and reinforcement learning opens the door for AI systems to learn directly from their experiences in a wide variety of environments, making it a versatile approach in areas where real-time decision-making is critical.