Model-based Reinforcement Learning

Model-based Reinforcement Learning is a method that combines model learning and reinforcement learning by constructing a dynamic model of the environment to predict future states and rewards, thereby optimizing the decision-making process. Its aim is to improve learning efficiency and generalization capabilities, reducing the reliance on large amounts of sample data. This approach has significant application value in areas such as robotics control, autonomous driving, and complex system management.