Lecture Materials

Lecture Materials

Lecture materials for this course are given below. Note the associated refresh your understanding and check your understanding polls will be posted weekly.
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Topic   Videos (on Canvas/Panopto)  Course Materials  
Introduction to Reinforcement Learning
  • Lecture 1
    		•
    1. Lecture 1 Draft Slides [Post class version]
    2. Additional Materials:
    Tabular MDP planning
  • Lecture 2
    		•
    1. Lecture 2 Slides (pre-class) [Post class, annotated]
    2. Additional Materials:
      • SB (Sutton and Barto) Chp 3, 4.1-4.4
    Tabular RL policy evaluation
  • Lecture 3
    		•
    1. Lecture 3 Slides (pre-class) [Post class, with annotations]
    2. Additional Materials:
      • SB (Sutton and Barto) Chp 5.1, 5.5, 6.1-6.3
      • David Silver's Lecture 4 [link]
    Q-learning
  • Lecture 4
    		•
    1. Lecture 4 Slides (preclass) (post class with annotations)
    2. Additional Materials:
      • SB (Sutton and Barto) Chp 5.2, 5.4, 6.4-6.5, 6.7
    Policy Gradient
  • Lecture 5
  • Lecture 6
  • Lecture 7
    		•
    1. Lecture 5 Slides [Post lecture with annotations]
    2. Lecture 6 Slides [Post class annotations]
    3. Lecture 7 Slides [Post class annotations]
    4. Additional Materials:
      • SB (Sutton and Barto) Chp 13
    Imitation Learning and Learning from Human Input
  • Lecture 8
  • Lecture 9 (including DPO guest lecture by Rafael Rafailov, Archit Sharma, Eric Mitchell)
  • Lecture 10
    		•
    1. Lecture 7 Slides [Post class annotations]
    2. Lecture 8 Slides (preclass) [Post class with annotations]
    3. Lecture 9 Slides [Post class]
    4. Lecture 9 DPO Slides
    5. Lecture 10 Slides [Post class]
    6. Additional Materials:
    Fast Learning / Data Efficient RL
  • Lecture 11
  • Lecture 12
  • Lecture 13
    		•
    1. Lecture 11 Slides [Post class, with annotations]
    2. Lecture 12 Slides [Post class, with annotations]
    3. Lecture 13 Slides [Post class, with annotations]
    4. Additional Materials:
    MCTS
  • Lecture 14
    		•
    1. Lecture 14 Slides [Post class, with annotations]
    Rewards in Reinforcement Learning
  • Lecture 15
    		•
  • Lecture 15 Slides (preclass) Post class with annotations
  • Lecture 15 (Value Alignment)
    • Review and Looking Forward
  • Lecture 16 Slides [post class]
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