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Topic · function approximation

function approximation

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

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