Deep Reinforcement Learning combines the principles of reinforcement learning with deep learning techniques to enable agents to learn from their interactions with an environment. By using deep neural networks to approximate value functions or policies, this approach allows agents to handle complex tasks and environments that require high-dimensional inputs. Deep Reinforcement Learning has achieved notable successes in areas such as game playing, robotic control, and autonomous systems, demonstrating its capability to learn and adapt to challenging scenarios.