Temporal Difference Explained – The Key to Q-Learning скачать в хорошем качестве

Temporal Difference Explained – The Key to Q-Learning

Full Course HERE 👉: https://sds.courses/ai-az How do AI agents learn from experience? In this video, we break down Temporal Difference Learning, the key concept behind Q-learning and reinforcement learning. You’ll learn why deterministic environments are easier to handle, how non-deterministic environments create challenges, and why AI updates its knowledge gradually using the Bellman Equation and learning rate adjustments. Course Link HERE: https://sds.courses/ai-az You can also find us here: Website: https://www.superdatascience.com/ Facebook:   / superdatascience   Twitter:   / superdatasci   LinkedIn:   / superdatascience   Contact us at: support@superdatascience.com If you’ve ever wondered how AI handles randomness and improves decision-making over time, this is the video for you! 🚀 Chapters 00:01 - Introduction to Temporal Difference 00:35 - Deterministic vs. Non-Deterministic AI 01:07 - Why Value Calculation is Hard in Stochastic Environments 02:41 - Recursive Nature of Value Estimation 04:12 - The Bellman Equation in Q-Learning 05:48 - Introducing Temporal Difference 06:59 - How AI Uses Temporal Difference to Learn 08:51 - Understanding the Update Formula for Q-Learning 10:29 - Why Learning Rate (Alpha) Matters 12:41 - The Formula That Drives AI Learning 14:23 - How AI Adapts Over Time 15:59 - When AI Stops Learning (Convergence Explained) 17:05 - Why AI Might Never Stop Learning 18:07 - Summary and Final Thoughts 18:41 - Further Reading on Temporal Difference Learning 📌 Learn More: 🔗 Learning to Predict by the Methods of Temporal Differences (1988) by Richard Sutton: https://link.springer.com/article/10.... 🔍 Hashtags: #MachineLearning #ArtificialIntelligence #ReinforcementLearning #Qlearning #TemporalDifference #AIAlgorithms #DeepLearning #DataScience #BellmanEquation #AIResearch #SmartAI #AIExplained #NeuralNetworks #LearningMachines #AITheory