4.1 Introduction to Dynamic Programming | DRL Course скачать в хорошем качестве

4.1 Introduction to Dynamic Programming | DRL Course

In this lesson, we introduce "Dynamic Programming (DP)" — the first family of methods for solving Reinforcement Learning (RL) problems exactly when a complete model of the environment is known. You'll learn: • The key idea behind dynamic programming: solving complex problems by breaking them into overlapping subproblems • How DP methods use the Bellman equations for policy evaluation and improvement • Why DP serves as the conceptual foundation for modern RL algorithms • The limitations of DP — including the need for a perfect model and the curse of dimensionality 📺 This lesson is part of the "Deep Reinforcement Learning (DRL) Course". Watch the full course here:    • Deep Reinforcement Learning (DRL) Course   #ReinforcementLearning #DynamicProgramming #BellmanEquation #MachineLearning #AI #DeepLearning