approximation: videos

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1. 1▶Session 21: Actor Critic based Policy Gradient, Safe RL, Planning, DYNA, Curriculum LearningJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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2. 2▶Session 20: Deep Neural Networks, MLP, Backpropagation, Policy Gradient, REINFORCEJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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3. 3▶Session 19: Asynchronous Q learning, Classification in ML, MLE, Logistic and Softmax RegressionJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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4. 4▶Session 18 Synchronous Q-learning, Model-free, based, tabular, with Linear Fn. Approx., ConvergenceJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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5. 5▶Session 17: Off-Policy Evaluation of TD0 with linear function Approximation, Emphatic TD0Jadavpur University, 2025: Introduction to Reinforcement LearningNotes
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6. 6▶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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7. 7▶Lesson 23: Network Algorithms and Approximations by Mohammad Hajiaghayi: Iterative Rounding Method 2Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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8. 8▶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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9. 9▶Lesson 22: Network Algorithms and Approximations by Mohammad Hajiaghayi: Iterative Rounding Method 1Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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10. 10▶Session 14: TD0 with linear function approximation, Glimpse at Stochastic Approximation Algorithm(1)Jadavpur University, 2025: Introduction to Reinforcement LearningNotes
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11. 11▶Lesson 21: Network Algorithms and Approximations by Mohammad Hajiaghayi:Con Facility & Group SteinerNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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12. 12▶Session 13: Function Approximation in RL, Policy Evaluation, SGD Monte Carlo, TD(0) ImplementationJadavpur University, 2025: Introduction to Reinforcement LearningNotes
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13. 13▶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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14. 14▶Lesson 20: Network Algorithms and Approximations by Mohammad Hajiaghayi: Metric Facility LocationNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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15. 15▶Lesson 19: Network Algorithms and Approximations by Mohammad Hajiaghayi: k-Center and k-MedianNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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16. 16▶Lesson 18: Network Algorithms and Approximations by Mohammad Hajiaghayi: Network Streaming 2Network Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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17. 17▶Lesson 17: Network Algorithms and Approximations by Mohammad Hajiaghayi: Network StreamingNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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18. 18▶Lesson 16: Network Algorithms and Approximations by Mohammad Hajiaghayi: Simplifying DecompositionsNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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19. 19▶Lesson 13: Network Algorithms and Approximations by Mohammad Hajiaghayi: Tree Embedding Cut ProblemsNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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20. 20▶Lesson 12: Network Algorithms and Approximations by Mohammad Hajiaghayi: Oblivious RoutingNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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21. 21▶Lesson 11: Network Algorithms and Approximations by Mohammad Hajiaghayi: Oblivious and UniversalNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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22. 22▶Lesson 9: Network Algorithms and Approximations by Mohammad Hajiaghayi: Uniform Buy-at-Bulk NetworkNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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23. 23▶Lesson 8: Network Algorithms and Approximations by Mohammad Hajiaghayi: Network Design ProblemsNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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24. 24▶Lesson 7: Network Algorithms and Approximations by Mohammad Hajiaghayi: FRT Embeddings into TreesNetwork Algorithms and Approximations Course by Mohammad HajiaghayiNotes
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25. 25▶Lecture 11, 2024: On-line training, neural networks, and other approximation architecturesReinforcement Learning Course at ASU, Spring, 2021Notes
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26. 26▶Lecture 4, 2024, POMDP, Systems with Changing Parameters, Adaptive Control, Model Predictive ControlReinforcement Learning Course at ASU, Spring, 2021Notes
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27. 27▶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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28. 28▶Further Contemporary RL Algorithms (TRPO, PPO - Lecture 13, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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29. 29▶Deterministic Policy Gradient Methods (Lecture 12, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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30. 30▶Stochastic Policy Gradient Methods (Lecture 11, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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31. 31▶Value-Based Control with Function Approximation (Lecture 10, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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32. 32▶On-Policy Prediction with Function Approximation (Lecture 09, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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33. 33▶Function Approximation with Supervised Learning (Lecture 08, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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34. 34▶Planning and Learning with Tabular Methods (Lecture 07, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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35. 35▶Multi-Step Bootstrapping (Lecture 06, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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36. 36▶Temporal Difference Learning (Lecture 05, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
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37. 37▶The Quantum Computing Advantage - Prof. Ryan O'DonnellRyan O'Donnell Theoretical Computer Science TalksNotes
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38. 38▶Lecture 13, 2021: An overview of the entire course. Discussion. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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39. 39▶Lecture 12, 2021: Aggregation methods and approximation in value space. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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40. 40▶Lecture 11, 2021: Linear programming, policy approximation, policy gradients. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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41. 41▶Lecture 10, 2021: Approximate policy iteration, Q-learning, parallel versions. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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42. 42▶Lecture 9, 2021: Infinite horizon theory and algorithms. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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43. 43▶Lecture 8, 2021: Neural networks, off-line training. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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44. 44▶Lecture 7, 2021: Constrained forms of rollout, discrete optimization, ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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45. 45▶Lecture 6, 2021: Model Predictive Control, ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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46. 46▶Ryan O'Donnell (CMU) -- Learning quantum statesRyan O'Donnell Theoretical Computer Science TalksNotes
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47. 47▶TCS+ Talk: Ryan O'DonnellRyan O'Donnell Theoretical Computer Science TalksNotes
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48. 48▶Explicit near-Ramanujan graphs of every degreeRyan O'Donnell Theoretical Computer Science TalksNotes
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49. 49▶Nonlinear approximation by deep ReLU networks - Ron DeVore, Texas A&MMathematics of data: Structured representations for sensing, approximation and learningNotes
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50. 50▶Two decentralised learning problems: Sketching and policy evaluation - Justin Romberg, Georgia TechMathematics of data: Structured representations for sensing, approximation and learningNotes
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