Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines the capabilities of deep learning with the principles of reinforcement learning. In reinforcement learning, an "agent" learns to make sequential decisions by interacting with an "environment" to maximize a cumulative "reward" signal. The agent receives observations of the environment's state, takes an action, and then receives a new state and a reward, learning optimal policies through trial and error. DRL integrates deep neural networks into this framework to handle complex, high-dimensional state and action spaces, which traditional reinforcement learning algorithms struggle with. For example, deep networks can process raw sensory inputs like images (e.g., from a game screen or a robot's camera) to extract meaningful features (the "state"), or to approximate value functions or policies directly. This combination has enabled DRL agents to achieve superhuman performance in complex domains such as playing Atari games (Deep Q-Networks), Go (AlphaGo), and controlling robotic systems, by allowing them to learn directly from raw sensor data without explicit feature engineering.