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gradient method

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  1. [UCLA RL-LLM] Chapter 2.4: In-context learning and instruction fine-tuningReinforcement Learning of Large Language ModelsNotes
  2. [UCLA RL-LLM] Chapter 2.3: Transformers II (modern transformers updates and sampling methods)Reinforcement Learning of Large Language ModelsNotes
  3. [UCLA RL-LLM] Chapter 2.2: Transformers I (BERT, GPT-1)Reinforcement Learning of Large Language ModelsNotes
  4. [UCLA RL-LLM] Chapter 2.1: NLP foundations, language modeling, RNNsReinforcement Learning of Large Language ModelsNotes
  5. [UCLA RL-LLM] Chapter 3.2: Reinforcement learning with verifiable rewards (RLVR)Reinforcement Learning of Large Language ModelsNotes
  6. [UCLA RL-LLM] Chapter 3.1: Reinforcement learning from human feedback (PPO, DPO)Reinforcement Learning of Large Language ModelsNotes
  7. [UCLA RL-LLM] Chapter 1.5: AlphaGo, test-time compute, and expert iterationReinforcement Learning of Large Language ModelsNotes
  8. [UCLA RL-LLM] Chapter 1.4: Deep policy gradient methods (PPO, GRPO)Reinforcement Learning of Large Language ModelsNotes
  9. [UCLA RL-LLM] Chapter 1.3: Deep policy gradient methods (A3C)Reinforcement Learning of Large Language ModelsNotes
  10. [UCLA RL-LLM] Chapter 1.2: Deep policy evaluationReinforcement Learning of Large Language ModelsNotes
  11. [UCLA RL-LLM] Chapter 1.1: MDP foundations, imitation learning, and value iterationReinforcement Learning of Large Language ModelsNotes
  12. [UCLA RL-LLM] Chapter 0: Course outline and prologueReinforcement Learning of Large Language ModelsNotes
  13. DSCC 435 OPT for ML - 15 Alternating Direction Method of MultipliersOptimization for Machine LearningNotes
  14. DSCC 435 OPT for ML - 14 Augmented LagrangianOptimization for Machine LearningNotes
  15. DSCC 435 OPT for ML - 13 Saddle Point ProblemOptimization for Machine LearningNotes
  16. DSCC 435 OPT for ML - 12 Generalized Inexact Proximal Point FrameworkOptimization for Machine LearningNotes
  17. DSCC 435 OPT for ML - 11 Inexact Proximal Point FrameworkOptimization for Machine LearningNotes
  18. DSCC 435 OPT for ML - 4 Gradient MethodOptimization for Machine LearningNotes
  19. DSCC 435 OPT for ML - 6 Subgradient MethodOptimization for Machine LearningNotes
  20. DSCC 435 OPT for ML - 3 ComplexityOptimization for Machine LearningNotes
  21. DSCC 435 OPT for ML - 10 Frank Wolfe MethodOptimization for Machine LearningNotes
  22. DSCC 435 OPT for ML - 7 Mirror DescentOptimization for Machine LearningNotes
  23. DSCC 435 OPT for ML - 9 Accelerated Gradient MethodOptimization for Machine LearningNotes
  24. DSCC 435 OPT for ML - 8 Proximal Gradient MethodOptimization for Machine LearningNotes
  25. DSCC 435 OPT for ML - 5 Projected Gradient MethodOptimization for Machine LearningNotes
  26. DSCC 435 OPT for ML - 2 ConvexityOptimization for Machine LearningNotes
  27. DSCC 435 OPT for ML - 1 IntroductionOptimization for Machine LearningNotes
  28. Outlook and Research Insights (Safe, Edge and Meta Reinforcement Learning - Lecture 14, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  29. Further Contemporary RL Algorithms (TRPO, PPO - Lecture 13, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  30. Deterministic Policy Gradient Methods (Lecture 12, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  31. Stochastic Policy Gradient Methods (Lecture 11, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  32. Value-Based Control with Function Approximation (Lecture 10, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  33. On-Policy Prediction with Function Approximation (Lecture 09, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  34. Function Approximation with Supervised Learning (Lecture 08, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  35. Planning and Learning with Tabular Methods (Lecture 07, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  36. Multi-Step Bootstrapping (Lecture 06, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  37. Temporal Difference Learning (Lecture 05, Summer 2023)Reinforcement Learning Course: Lectures (Summer 2023)Notes
  38. Lecture 01 IntroductionCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  39. Lecture 02 Markov Decision ProcessesCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  40. Lecture 03 Solving known MDPsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  41. Lecture 04 Solving Known MDPsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  42. Lecture 05 Monte Carlo MethodsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  43. Lecture 06 Temporal Difference MethodCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  44. Lecture 07 Neural Networks Architectures for RLCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  45. Lecture 08 Function Approximation for PredictionCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  46. Lecture 09 Value FunctionCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes
  47. Lecture 10 Policy Gradient MethodsCMU: 2018 Fall: 10-703 Deep Reinforcement Learning and ControlNotes

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