5.3 Monte Carlo Control | DRL Course скачать в хорошем качестве

5.3 Monte Carlo Control | DRL Course

In this lesson, we introduce "Monte Carlo (MC) Control", where we go beyond prediction and start learning policies that improve through experience. You’ll learn: • How Monte Carlo control applies the Generalized Policy Iteration (GPI) idea • Why MC control needs exploration to discover better actions • The concept of “Exploring Starts” and why it matters • How epsilon-soft policies enable learning while maintaining exploration • The on-policy First-Visit Monte Carlo control approach 📺 This lesson is part of the "Deep Reinforcement Learning (DRL) Course". Watch the full course here:    • Deep Reinforcement Learning (DRL) Course   📘 Study materials: [link to Reinforcement Learning Study Guide and resources] #ReinforcementLearning #MonteCarlo #Control #MachineLearning #AI #DeepLearning