3.3 Policies and Value Functions | DRL Course скачать в хорошем качестве

3.3 Policies and Value Functions | DRL Course

In this lesson, we dive into "Policies and Value Functions", two of the most important concepts in Reinforcement Learning (RL). You'll learn: • What a policy (π) is and how it defines an agent's behavior • The difference between deterministic and stochastic policies • How value functions estimate long-term expected rewards • The meaning of state-value (vπ) and action-value (qπ) functions • How the Bellman equation expresses relationships between states, actions, and future values 📺 This lesson is part of the "Deep Reinforcement Learning (DRL) Course". Watch the full playlist here:    • Плейлист   📘 Study materials: [link to Reinforcement Learning Study Guide and resources] #ReinforcementLearning #BellmanEquation #MachineLearning #AI #DeepLearning