function approximation: videos

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1. 1▶Lecture 14 - REINFORCE | Reinforcement Learning Phase|Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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2. 2▶Lecture 13 - Policy Gradient Methods | Reinforcement Learning Phase | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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3. 3▶Session 21: Actor Critic based Policy Gradient, Safe RL, Planning, DYNA, Curriculum LearningJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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4. 4▶Lecture 12 - Policy Control using Value Function Approximation | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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5. 5▶Session 20: Deep Neural Networks, MLP, Backpropagation, Policy Gradient, REINFORCEJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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6. 6▶Session 19: Asynchronous Q learning, Classification in ML, MLE, Logistic and Softmax RegressionJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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7. 7▶Lecture 11 - Function Approximation Methods|Reinforcement Learning Phase|Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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8. 8▶Lecture 10 -Temporal Difference Control | Reinforcement Learning Phase | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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9. 9▶Session 18 Synchronous Q-learning, Model-free, based, tabular, with Linear Fn. Approx., ConvergenceJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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10. 10▶Lecture 9 - Temporal Difference Prediction|Reinforcement Learning Phase| Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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11. 11▶Session 17: Off-Policy Evaluation of TD0 with linear function Approximation, Emphatic TD0Jadavpur University, 2025: Introduction to Reinforcement LearningNotes
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12. 12▶Lecture 8 - Monte Carlo Methods | Reinforcement Learning Phase | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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13. 13▶Session 16 γ contraction, Banach's Fixed Point Theorem, How far is it far from the intended optimalJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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14. 14▶Lecture 7 - Dynamic Programming | Reinforcement Learning Phase | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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15. 15▶Session 15 TD(0) convergence proof (contd), Point of Convergence of TD(0) (linear function approx.)Jadavpur University, 2025: Introduction to Reinforcement LearningNotes
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16. 16▶Lecture 6 - Value Functions | Reinforcement Learning | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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17. 17▶Session 14: TD0 with linear function approximation, Glimpse at Stochastic Approximation Algorithm(1)Jadavpur University, 2025: Introduction to Reinforcement LearningNotes
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18. 18▶Lecture 5 - Markov Decision Processes | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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19. 19▶Session 13: Function Approximation in RL, Policy Evaluation, SGD Monte Carlo, TD(0) ImplementationJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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20. 20▶Session 12: On Policy vs Off Policy Algorithms, Importance Sampling, Model-free Q learning, SARSAJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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21. 21▶Lecture 4b - Multi-Arm Bandits | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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22. 22▶Lecture 4 - Reinforcement Learning - Basics | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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23. 23▶Lecture 3 - Verifiers - Beam Search | Reasoning LLMs from ScratchReasoning LLMs from ScratchNotes
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24. 24▶Lecture 2 - Chain of Thought Reasoning | Reasoning LLMs from Scratch SeriesReasoning LLMs from ScratchNotes
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25. 25▶Lecture 1 - Reasoning LLMs from Scratch - Series IntroductionReasoning LLMs from ScratchNotes
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26. 26▶Lecture 22: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning IIPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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27. 27▶Lecture 21: Foundations of Reinforcement Learning: Partially Observable Reinforcement Learning IPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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28. 28▶Lecture 20: Foundations of Reinforcement Learning: Multiplayer General-Sum GamesPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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29. 29▶Lecture 19: Foundations of Reinforcement Learning: Two-Player Zero-Sum GamesPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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30. 30▶Lecture 17: Foundations of Reinforcement Learning: Exploration in General Function ApproximationPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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31. 31▶Lecture 18: Foundations of Reinforcement Learning: Multiagent Reinforcement LearningPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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32. 32▶Lecture 11: Foundations of Reinforcement Learning: Lower Bounds for MDPPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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33. 33▶Lecture 14: Foundations of Reinforcement Learning: Least-Squares Value IterationPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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34. 34▶Lecture 13: Foundations of Reinforcement Learning: RL in Large State SpacePrinceton University Lectures - Foundations of Reinforcement LearningNotes
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35. 35▶Lecture 12: Foundations of Reinforcement Learning: Offline RLPrinceton University Lectures - Foundations of Reinforcement LearningNotes
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36. 36▶Outlook and Research Insights (Safe, Edge and Meta Reinforcement Learning - Lecture 14, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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37. 37▶Further Contemporary RL Algorithms (TRPO, PPO - Lecture 13, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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38. 38▶Deterministic Policy Gradient Methods (Lecture 12, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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39. 39▶Stochastic Policy Gradient Methods (Lecture 11, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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40. 40▶Value-Based Control with Function Approximation (Lecture 10, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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41. 41▶On-Policy Prediction with Function Approximation (Lecture 09, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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42. 42▶Function Approximation with Supervised Learning (Lecture 08, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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43. 43▶Planning and Learning with Tabular Methods (Lecture 07, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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44. 44▶Multi-Step Bootstrapping (Lecture 06, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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45. 45▶Temporal Difference Learning (Lecture 05, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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