DRL is a type of machine learning where an agent learns to make decisions by trial and error, guided by rewards or penalties, using deep neural networks. Unlike traditional methods, which struggle with complex environments, DRL allows machines to learn directly from raw data, like images or game screens. The neural network helps the agent recognize patterns and improve its decisions over time. DRL has achieved impressive results in tasks like playing video games (e.g., Atari, AlphaGo), controlling robots, and developing self-driving cars, making it a powerful tool for solving real-world problems involving sequential decision-making