Deep Reinforcement Learning (DRL) is a combination of two powerful concepts: deep learning and reinforcement learning. In DRL, an agent learns to make decisions by interacting with an environment. It receives feedback in the form of rewards or penalties based on its actions, helping it understand which choices lead to better outcomes over time. The "deep" part comes from using neural networks to process complex data, allowing the agent to learn from high-dimensional inputs like images or sounds. This approach has led to impressive achievements, such as AlphaGo defeating human champions in the game of Go, showcasing the potential of DRL in solving challenging problems.