Скачать или смотреть 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.

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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