Deep reinforcement learning is a subfield of machine learning that combines deep learning techniques with reinforcement learning principles to create intelligent systems capable of learning from their environment through trial and error. It involves training an artificial neural network, also known as a deep neural network, to make decisions and take actions based on reward or punishment signals received from the environment. By employing deep neural networks, which are highly effective at learning patterns and extracting features from input data, deep reinforcement learning algorithms can handle high-dimensional state spaces and complex tasks. This enables the creation of AI agents that can navigate and solve challenging problems in different domains, such as robotics, game playing, and autonomous driving.