Deep Reinforcement Learning

Deep reinforcement learning is an advanced method that combines deep learning and reinforcement learning, aiming to automatically learn optimal decision-making strategies through the interaction between an agent and its environment. Its core objective is to enable the agent to maximize cumulative rewards in complex and dynamic environments, thereby achieving efficient autonomous learning and adaptability. Deep reinforcement learning has significant application value in areas such as gaming, robotics, and autonomous driving, capable of addressing high-dimensional state spaces and nonlinear problems that are difficult for traditional methods to handle.